{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Base imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "from torch.optim import Optimizer\n",
    "\n",
    "\n",
    "import collections\n",
    "import h5py, sys\n",
    "import gzip\n",
    "import os\n",
    "import math\n",
    "\n",
    "\n",
    "try:\n",
    "    import cPickle as pickle\n",
    "except:\n",
    "    import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdir(paths):\n",
    "    if not isinstance(paths, (list, tuple)):\n",
    "        paths = [paths]\n",
    "    for path in paths:\n",
    "        if not os.path.isdir(path):\n",
    "            os.makedirs(path)\n",
    "\n",
    "from __future__ import print_function\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "from torch.autograd import Variable\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "\n",
    "suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n",
    "def humansize(nbytes):\n",
    "    i = 0\n",
    "    while nbytes >= 1024 and i < len(suffixes)-1:\n",
    "        nbytes /= 1024.\n",
    "        i += 1\n",
    "    f = ('%.2f' % nbytes)\n",
    "    return '%s%s' % (f, suffixes[i])\n",
    "\n",
    "\n",
    "def get_num_batches(nb_samples, batch_size, roundup=True):\n",
    "    if roundup:\n",
    "        return ((nb_samples + (-nb_samples % batch_size)) / batch_size)  # roundup division\n",
    "    else:\n",
    "        return nb_samples / batch_size\n",
    "\n",
    "def generate_ind_batch(nb_samples, batch_size, random=True, roundup=True):\n",
    "    if random:\n",
    "        ind = np.random.permutation(nb_samples)\n",
    "    else:\n",
    "        ind = range(int(nb_samples))\n",
    "    for i in range(int(get_num_batches(nb_samples, batch_size, roundup))):\n",
    "        yield ind[i * batch_size: (i + 1) * batch_size]\n",
    "\n",
    "def to_variable(var=(), cuda=True, volatile=False):\n",
    "    out = []\n",
    "    for v in var:\n",
    "        if isinstance(v, np.ndarray):\n",
    "            v = torch.from_numpy(v).type(torch.FloatTensor)\n",
    "\n",
    "        if not v.is_cuda and cuda:\n",
    "            v = v.cuda()\n",
    "\n",
    "        if not isinstance(v, Variable):\n",
    "            v = Variable(v, volatile=volatile)\n",
    "\n",
    "        out.append(v)\n",
    "    return out\n",
    "  \n",
    "def cprint(color, text, **kwargs):\n",
    "    if color[0] == '*':\n",
    "        pre_code = '1;'\n",
    "        color = color[1:]\n",
    "    else:\n",
    "        pre_code = ''\n",
    "    code = {\n",
    "        'a': '30',\n",
    "        'r': '31',\n",
    "        'g': '32',\n",
    "        'y': '33',\n",
    "        'b': '34',\n",
    "        'p': '35',\n",
    "        'c': '36',\n",
    "        'w': '37'\n",
    "    }\n",
    "    print(\"\\x1b[%s%sm%s\\x1b[0m\" % (pre_code, code[color], text), **kwargs)\n",
    "    sys.stdout.flush()\n",
    "\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = np.random.get_state()\n",
    "    np.random.shuffle(a)\n",
    "    np.random.set_state(rng_state)\n",
    "    np.random.shuffle(b)\n",
    "    \n",
    "    \n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataloader functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Datafeed(data.Dataset):\n",
    "\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)\n",
    "\n",
    "class DatafeedImage(data.Dataset):\n",
    "    def __init__(self, x_train, y_train, transform=None):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.transform = transform\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img = self.x_train[index]\n",
    "        img = Image.fromarray(np.uint8(img))\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "        return img, self.y_train[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "class BaseNet(object):\n",
    "    def __init__(self):\n",
    "        cprint('c', '\\nNet:')\n",
    "\n",
    "    def get_nb_parameters(self):\n",
    "        return np.sum(p.numel() for p in self.model.parameters())\n",
    "\n",
    "    def set_mode_train(self, train=True):\n",
    "        if train:\n",
    "            self.model.train()\n",
    "        else:\n",
    "            self.model.eval()\n",
    "\n",
    "    def update_lr(self, epoch, gamma=0.99):\n",
    "        self.epoch += 1\n",
    "        if self.schedule is not None:\n",
    "            if len(self.schedule) == 0 or epoch in self.schedule:\n",
    "                self.lr *= gamma\n",
    "                print('learning rate: %f  (%d)\\n' % self.lr, epoch)\n",
    "                for param_group in self.optimizer.param_groups:\n",
    "                    param_group['lr'] = self.lr\n",
    "\n",
    "    def save(self, filename):\n",
    "        cprint('c', 'Writting %s\\n' % filename)\n",
    "        torch.save({\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'model': self.model,\n",
    "            'optimizer': self.optimizer}, filename)\n",
    "\n",
    "    def load(self, filename):\n",
    "        cprint('c', 'Reading %s\\n' % filename)\n",
    "        state_dict = torch.load(filename)\n",
    "        self.epoch = state_dict['epoch']\n",
    "        self.lr = state_dict['lr']\n",
    "        self.model = state_dict['model']\n",
    "        self.optimizer = state_dict['optimizer']\n",
    "        print('  restoring epoch: %d, lr: %f' % (self.epoch, self.lr))\n",
    "        return self.epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear_2L(nn.Module):\n",
    "    def __init__(self, input_dim, output_dim):\n",
    "        super(Linear_2L, self).__init__()\n",
    "        \n",
    "        n_hid = 1200\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_dim, n_hid)\n",
    "        self.fc2 = nn.Linear(n_hid, n_hid)\n",
    "        self.fc3 = nn.Linear(n_hid, output_dim)\n",
    "        \n",
    "        # choose your non linearity\n",
    "        #self.act = nn.Tanh()\n",
    "        #self.act = nn.Sigmoid()\n",
    "        self.act = nn.ReLU(inplace=True)\n",
    "        #self.act = nn.ELU(inplace=True)\n",
    "        #self.act = nn.SELU(inplace=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "\n",
    "        x = x.view(-1, self.input_dim) # view(batch_size, input_dim)\n",
    "        # -----------------\n",
    "        x = self.fc1(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        x = self.fc2(x)\n",
    "        # -----------------\n",
    "        x = self.act(x)\n",
    "        # -----------------\n",
    "        y = self.fc3(x)\n",
    "\n",
    "        return y\n",
    "    \n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_groups = list(model.parameters())\n",
    "\n",
    "# for param in param_groups:\n",
    "#     print(param.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom SGLD optimiser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.optim.optimizer import Optimizer, required\n",
    "class SGLD(Optimizer):\n",
    "    \"\"\"\n",
    "    SGLD optimiser based on pytorch's SGD. \n",
    "    Note that the weight decay is specified in terms of the gaussian prior sigma\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, params, lr=required, norm_sigma=0, addnoise=True):\n",
    "        \n",
    "        weight_decay = 1/(norm_sigma**2)\n",
    "        \n",
    "        if weight_decay < 0.0:\n",
    "            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n",
    "        if lr is not required and lr < 0.0:\n",
    "            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n",
    "        \n",
    "        defaults = dict(lr=lr, weight_decay=weight_decay, addnoise=addnoise)\n",
    "        \n",
    "        super(SGLD, self).__init__(params, defaults)\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"\n",
    "        Performs a single optimization step.\n",
    "        \"\"\"\n",
    "        loss = None\n",
    "        \n",
    "        for group in self.param_groups:\n",
    "\n",
    "            weight_decay = group['weight_decay']\n",
    "            \n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                d_p = p.grad.data\n",
    "                if weight_decay != 0:\n",
    "                    d_p.add_(weight_decay, p.data)\n",
    "                    \n",
    "                if group['addnoise']:\n",
    "                    \n",
    "                    langevin_noise = p.data.new(p.data.size()).normal_(mean=0, std=1)/np.sqrt(group['lr'])\n",
    "                    p.data.add_(-group['lr'],\n",
    "                                0.5*d_p + langevin_noise)\n",
    "                else:\n",
    "                    p.data.add_(-group['lr'], 0.5*d_p)\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pSGLD optimiser\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class pSGLD(Optimizer):\n",
    "    \"\"\"\n",
    "    RMSprop preconditioned SGLD using pytorch rmsprop implementation.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, params, lr=required, norm_sigma=0, alpha=0.99, eps=1e-8, centered=False, addnoise=True):\n",
    "        \n",
    "        weight_decay = 1/(norm_sigma**2)\n",
    "        \n",
    "        if weight_decay < 0.0:\n",
    "            raise ValueError(\"Invalid weight_decay value: {}\".format(weight_decay))\n",
    "        if lr is not required and lr < 0.0:\n",
    "            raise ValueError(\"Invalid learning rate: {}\".format(lr))\n",
    "        defaults = dict(lr=lr, weight_decay=weight_decay, alpha=alpha, eps=eps, centered=centered, addnoise=addnoise)\n",
    "        super(pSGLD, self).__init__(params, defaults)\n",
    "        \n",
    "    def __setstate__(self, state):\n",
    "        super(pSGLD, self).__setstate__(state)\n",
    "        for group in self.param_groups:\n",
    "            group.setdefault('centered', False)\n",
    "\n",
    "    def step(self):\n",
    "        \"\"\"\n",
    "        Performs a single optimization step.\n",
    "        \"\"\"\n",
    "        loss = None\n",
    "\n",
    "        for group in self.param_groups:\n",
    "            \n",
    "            weight_decay = group['weight_decay']\n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                d_p = p.grad.data\n",
    "                \n",
    "                state = self.state[p]\n",
    "                \n",
    "                if len(state) == 0:\n",
    "                    state['step'] = 0\n",
    "                    state['square_avg'] = torch.zeros_like(p.data)\n",
    "                    if group['centered']:\n",
    "                        state['grad_avg'] = torch.zeros_like(p.data)\n",
    "                        \n",
    "                square_avg = state['square_avg']\n",
    "                alpha = group['alpha']\n",
    "                state['step'] += 1\n",
    "                \n",
    "                if weight_decay != 0:\n",
    "                    d_p.add_(weight_decay, p.data)\n",
    "                \n",
    "                # sqavg x alpha + (1-alph) sqavg *(elemwise) sqavg\n",
    "                square_avg.mul_(alpha).addcmul_(1-alpha, d_p, d_p)\n",
    "                \n",
    "                if group['centered']:\n",
    "                    grad_avg = state['grad_avg']\n",
    "                    grad_avg.mul_(alpha).add_(1-alpha, d_p)\n",
    "                    avg = square_avg.cmul(-1, grad_avg, grad_avg).sqrt().add_(group['eps'])\n",
    "                else:\n",
    "                    avg = square_avg.sqrt().add_(group['eps'])\n",
    "                    \n",
    "#                 print(avg.shape)\n",
    "                if group['addnoise']:\n",
    "                    langevin_noise = p.data.new(p.data.size()).normal_(mean=0, std=1)/np.sqrt(group['lr'])\n",
    "                    p.data.add_(-group['lr'],\n",
    "                                0.5*d_p.div_(avg) + langevin_noise/torch.sqrt(avg))\n",
    "                    \n",
    "                else:\n",
    "                    p.data.addcdiv_(-group['lr'], 0.5*d_p, avg)\n",
    "\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import copy\n",
    "\n",
    "class Net_langevin(BaseNet):\n",
    "    eps = 1e-6\n",
    "\n",
    "    def __init__(self, lr=1e-3, channels_in=3, side_in=28, cuda=True, classes=10, N_train=60000, prior_sig=0):\n",
    "        super(Net_langevin, self).__init__()\n",
    "        cprint('y', ' Creating Net!! ')\n",
    "        self.lr = lr\n",
    "        self.schedule = None  # [] #[50,200,400,600]\n",
    "        self.cuda = cuda\n",
    "        self.channels_in = channels_in\n",
    "        self.prior_sig = prior_sig\n",
    "        self.classes = classes\n",
    "        self.N_train = N_train\n",
    "        self.side_in=side_in\n",
    "        self.create_net()\n",
    "        self.create_opt()\n",
    "        self.epoch = 0\n",
    "        \n",
    "        self.weight_set_samples = []\n",
    "\n",
    "        self.test=False\n",
    "\n",
    "    def create_net(self):\n",
    "        torch.manual_seed(42)\n",
    "        if self.cuda:\n",
    "            torch.cuda.manual_seed(42)\n",
    "\n",
    "        self.model = Linear_2L(input_dim=self.channels_in*self.side_in*self.side_in, output_dim=self.classes)\n",
    "        if self.cuda:\n",
    "            self.model.cuda()\n",
    "#             cudnn.benchmark = True\n",
    "\n",
    "        print('    Total params: %.2fM' % (self.get_nb_parameters() / 1000000.0))\n",
    "\n",
    "    def create_opt(self):\n",
    "#         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, betas=(0.9, 0.999), eps=1e-08,\n",
    "#                                           weight_decay=0)\n",
    "#         self.optimizer = SGLD(params=self.model.parameters(), lr=self.lr, norm_sigma=self.prior_sig, addnoise=True)\n",
    "        self.optimizer = pSGLD(params=self.model.parameters(), lr=self.lr, norm_sigma=self.prior_sig, addnoise=True)\n",
    "\n",
    "    #         self.optimizer = torch.optim.SGD(self.model.parameters(), lr=self.lr, momentum=0.9)\n",
    "#         self.sched = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=1, gamma=10, last_epoch=-1)\n",
    "\n",
    "    def fit(self, x, y):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        out = self.model(x)\n",
    "        loss = F.cross_entropy(out, y, reduction='mean') # We use mean because we treat as an estimation of whole dataset\n",
    "        loss = loss * self.N_train \n",
    "            \n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        # out: (batch_size, out_channels, out_caps_dims)\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data*x.shape[0]/self.N_train, err\n",
    "\n",
    "    def eval(self, x, y, train=False):\n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "\n",
    "        out = self.model(x)\n",
    "\n",
    "        loss = F.cross_entropy(out, y, reduction='sum')\n",
    "\n",
    "        probs = F.softmax(out, dim=1).data.cpu()\n",
    "\n",
    "        pred = out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    def save_sampled_net(self, max_samples):\n",
    "        \n",
    "        if len(self.weight_set_samples) >= max_samples:\n",
    "            self.weight_set_samples.pop(0)\n",
    "            \n",
    "        self.weight_set_samples.append(copy.deepcopy(self.model.state_dict()))\n",
    "        \n",
    "        cprint('c', ' saving weight samples %d/%d' % (len(self.weight_set_samples), max_samples) )\n",
    "        \n",
    "        return None\n",
    "        \n",
    "    def sample_eval(self, x, y, Nsamples=0, logits=True, train=False):\n",
    "        if Nsamples == 0:\n",
    "            Nsamples = len(self.weight_set_samples)\n",
    "            \n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "        \n",
    "        out = x.data.new(Nsamples, x.shape[0], self.classes)\n",
    "        \n",
    "        # iterate over all saved weight configuration samples\n",
    "        for idx, weight_dict in enumerate(self.weight_set_samples):\n",
    "            if idx == Nsamples:\n",
    "                break\n",
    "            self.model.load_state_dict(weight_dict)\n",
    "            out[idx] = self.model(x)\n",
    "        \n",
    "        \n",
    "        if logits:\n",
    "            mean_out = out.mean(dim=0, keepdim=False)\n",
    "            loss = F.cross_entropy(mean_out, y, reduction='sum')\n",
    "            probs = F.softmax(mean_out, dim=1).data.cpu()\n",
    "            \n",
    "        else:\n",
    "            mean_out =  F.softmax(out, dim=2).mean(dim=0, keepdim=False)\n",
    "            probs = mean_out.data.cpu()\n",
    "            \n",
    "            log_mean_probs_out = torch.log(mean_out)\n",
    "            loss = F.nll_loss(log_mean_probs_out, y, reduction='sum')\n",
    "\n",
    "        pred = mean_out.data.max(dim=1, keepdim=False)[1]  # get the index of the max log-probability\n",
    "        err = pred.ne(y.data).sum()\n",
    "\n",
    "        return loss.data, err, probs\n",
    "    \n",
    "    \n",
    "    def all_sample_eval(self, x, y, Nsamples):\n",
    "        if Nsamples == 0:\n",
    "            Nsamples = len(self.weight_set_samples)\n",
    "            \n",
    "        x, y = to_variable(var=(x, y.long()), cuda=self.cuda)\n",
    "        \n",
    "        out = x.data.new(Nsamples, x.shape[0], self.classes)\n",
    "        \n",
    "        # iterate over all saved weight configuration samples\n",
    "        for idx, weight_dict in enumerate(self.weight_set_samples):\n",
    "            if idx == Nsamples:\n",
    "                break\n",
    "            self.model.load_state_dict(weight_dict)\n",
    "            out[idx] = self.model(x)\n",
    "        \n",
    "        prob_out =  F.softmax(out, dim=2)\n",
    "        prob_out = prob_out.data\n",
    "\n",
    "        return prob_out\n",
    "    \n",
    "    \n",
    "    def get_weight_samples(self, Nsamples=0):\n",
    "        weight_vec = []\n",
    "        \n",
    "        if Nsamples == 0 or Nsamples > len(self.weight_set_samples):\n",
    "            Nsamples = len(self.weight_set_samples)\n",
    "            \n",
    "        for idx, state_dict in enumerate(self.weight_set_samples):\n",
    "            if idx == Nsamples:\n",
    "                break\n",
    "                \n",
    "            for key in state_dict.keys():\n",
    "                if 'weight' in key:\n",
    "                    weight_mtx = state_dict[key].cpu()\n",
    "                    for weight in weight_mtx.view(-1):\n",
    "                        weight_vec.append(weight)\n",
    "            \n",
    "        return np.array(weight_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2260125\n",
      "0.19845\n"
     ]
    }
   ],
   "source": [
    "factor = 1.05\n",
    "print(0.21525*factor)\n",
    "print(0.189*factor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m\n",
      "Data:\u001b[0m\n",
      "\u001b[36m\n",
      "Network:\u001b[0m\n",
      "\u001b[36m\n",
      "Net:\u001b[0m\n",
      "\u001b[33m Creating Net!! \u001b[0m\n",
      "    Total params: 2.40M\n",
      "\u001b[36m\n",
      "Train:\u001b[0m\n",
      "  init cost variables:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.from_iter(generator)) or the python sum builtin instead.\n",
      "  import sys\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 0/200, Jtr_pred = 0.327787, err = 0.091683, \u001b[31m   time: 4.461258 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.217305, err = 0.062300\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 1/200, Jtr_pred = 0.195617, err = 0.056317, \u001b[31m   time: 4.542889 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171833, err = 0.050500\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 2/200, Jtr_pred = 0.161282, err = 0.046600, \u001b[31m   time: 4.570214 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.145802, err = 0.042700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 3/200, Jtr_pred = 0.141074, err = 0.041533, \u001b[31m   time: 4.522334 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.138701, err = 0.042600\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 4/200, Jtr_pred = 0.126658, err = 0.037650, \u001b[31m   time: 4.472240 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.137538, err = 0.042400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 5/200, Jtr_pred = 0.117213, err = 0.035683, \u001b[31m   time: 4.460010 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.129083, err = 0.038700\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 6/200, Jtr_pred = 0.109666, err = 0.033367, \u001b[31m   time: 4.409143 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.126305, err = 0.038900\n",
      "\u001b[0m\n",
      "it 7/200, Jtr_pred = 0.105924, err = 0.032750, \u001b[31m   time: 4.390324 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.139002, err = 0.043900\n",
      "\u001b[0m\n",
      "it 8/200, Jtr_pred = 0.101910, err = 0.032200, \u001b[31m   time: 4.701828 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.124494, err = 0.037400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 9/200, Jtr_pred = 0.095575, err = 0.030633, \u001b[31m   time: 4.719985 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.143072, err = 0.041800\n",
      "\u001b[0m\n",
      "it 10/200, Jtr_pred = 0.094543, err = 0.029867, \u001b[31m   time: 4.576064 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.122493, err = 0.037600\n",
      "\u001b[0m\n",
      "it 11/200, Jtr_pred = 0.089269, err = 0.028550, \u001b[31m   time: 4.682007 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140164, err = 0.040600\n",
      "\u001b[0m\n",
      "it 12/200, Jtr_pred = 0.086639, err = 0.027183, \u001b[31m   time: 4.424205 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.132582, err = 0.040500\n",
      "\u001b[0m\n",
      "it 13/200, Jtr_pred = 0.083591, err = 0.026633, \u001b[31m   time: 4.629366 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.130494, err = 0.040400\n",
      "\u001b[0m\n",
      "it 14/200, Jtr_pred = 0.082058, err = 0.026183, \u001b[31m   time: 4.469428 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.137400, err = 0.042400\n",
      "\u001b[0m\n",
      "it 15/200, Jtr_pred = 0.080516, err = 0.025800, \u001b[31m   time: 4.462988 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140402, err = 0.040100\n",
      "\u001b[0m\n",
      "it 16/200, Jtr_pred = 0.077578, err = 0.024950, \u001b[31m   time: 4.554068 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 1/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.133706, err = 0.039100\n",
      "\u001b[0m\n",
      "it 17/200, Jtr_pred = 0.076087, err = 0.025233, \u001b[31m   time: 4.564780 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.140239, err = 0.038700\n",
      "\u001b[0m\n",
      "it 18/200, Jtr_pred = 0.074556, err = 0.024450, \u001b[31m   time: 4.374141 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 2/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.137118, err = 0.040300\n",
      "\u001b[0m\n",
      "it 19/200, Jtr_pred = 0.073743, err = 0.024683, \u001b[31m   time: 4.390059 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.128864, err = 0.036800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 20/200, Jtr_pred = 0.072894, err = 0.024483, \u001b[31m   time: 4.552781 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 3/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.138589, err = 0.037100\n",
      "\u001b[0m\n",
      "it 21/200, Jtr_pred = 0.073355, err = 0.023917, \u001b[31m   time: 4.611609 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.132680, err = 0.034800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 22/200, Jtr_pred = 0.071899, err = 0.023783, \u001b[31m   time: 4.536256 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 4/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.129846, err = 0.034400\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 23/200, Jtr_pred = 0.069966, err = 0.022933, \u001b[31m   time: 4.544746 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.148968, err = 0.039000\n",
      "\u001b[0m\n",
      "it 24/200, Jtr_pred = 0.069523, err = 0.023133, \u001b[31m   time: 4.593852 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 5/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.131925, err = 0.038300\n",
      "\u001b[0m\n",
      "it 25/200, Jtr_pred = 0.067099, err = 0.022350, \u001b[31m   time: 4.559020 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.139380, err = 0.038200\n",
      "\u001b[0m\n",
      "it 26/200, Jtr_pred = 0.068420, err = 0.022967, \u001b[31m   time: 4.425620 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 6/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.143155, err = 0.039500\n",
      "\u001b[0m\n",
      "it 27/200, Jtr_pred = 0.067724, err = 0.023100, \u001b[31m   time: 4.565227 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.139887, err = 0.036500\n",
      "\u001b[0m\n",
      "it 28/200, Jtr_pred = 0.065803, err = 0.022067, \u001b[31m   time: 4.406875 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 7/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.160410, err = 0.042800\n",
      "\u001b[0m\n",
      "it 29/200, Jtr_pred = 0.065662, err = 0.021950, \u001b[31m   time: 4.607618 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.155237, err = 0.039000\n",
      "\u001b[0m\n",
      "it 30/200, Jtr_pred = 0.066264, err = 0.022067, \u001b[31m   time: 4.614420 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 8/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.146957, err = 0.038600\n",
      "\u001b[0m\n",
      "it 31/200, Jtr_pred = 0.066125, err = 0.020900, \u001b[31m   time: 4.577000 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.145471, err = 0.038300\n",
      "\u001b[0m\n",
      "it 32/200, Jtr_pred = 0.064638, err = 0.021783, \u001b[31m   time: 4.604031 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 9/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.155025, err = 0.041700\n",
      "\u001b[0m\n",
      "it 33/200, Jtr_pred = 0.064738, err = 0.021633, \u001b[31m   time: 4.396810 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.161974, err = 0.042400\n",
      "\u001b[0m\n",
      "it 34/200, Jtr_pred = 0.062832, err = 0.021167, \u001b[31m   time: 4.506111 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 10/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.157909, err = 0.039900\n",
      "\u001b[0m\n",
      "it 35/200, Jtr_pred = 0.063101, err = 0.020750, \u001b[31m   time: 4.549760 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152981, err = 0.039100\n",
      "\u001b[0m\n",
      "it 36/200, Jtr_pred = 0.064798, err = 0.021517, \u001b[31m   time: 4.496335 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 11/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151149, err = 0.039400\n",
      "\u001b[0m\n",
      "it 37/200, Jtr_pred = 0.065313, err = 0.022017, \u001b[31m   time: 4.499622 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.151000, err = 0.037600\n",
      "\u001b[0m\n",
      "it 38/200, Jtr_pred = 0.062216, err = 0.021233, \u001b[31m   time: 4.565038 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 12/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.153277, err = 0.038300\n",
      "\u001b[0m\n",
      "it 39/200, Jtr_pred = 0.061789, err = 0.021217, \u001b[31m   time: 4.493787 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169497, err = 0.042700\n",
      "\u001b[0m\n",
      "it 40/200, Jtr_pred = 0.061836, err = 0.020383, \u001b[31m   time: 4.448216 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 13/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162527, err = 0.039700\n",
      "\u001b[0m\n",
      "it 41/200, Jtr_pred = 0.060455, err = 0.020400, \u001b[31m   time: 4.526823 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.152240, err = 0.038300\n",
      "\u001b[0m\n",
      "it 42/200, Jtr_pred = 0.063512, err = 0.021067, \u001b[31m   time: 4.601447 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 14/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.150161, err = 0.036000\n",
      "\u001b[0m\n",
      "it 43/200, Jtr_pred = 0.061499, err = 0.020300, \u001b[31m   time: 4.740883 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177914, err = 0.043600\n",
      "\u001b[0m\n",
      "it 44/200, Jtr_pred = 0.060853, err = 0.019967, \u001b[31m   time: 4.588886 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 15/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162322, err = 0.039900\n",
      "\u001b[0m\n",
      "it 45/200, Jtr_pred = 0.060461, err = 0.020433, \u001b[31m   time: 4.456843 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.147318, err = 0.035900\n",
      "\u001b[0m\n",
      "it 46/200, Jtr_pred = 0.059951, err = 0.020167, \u001b[31m   time: 4.504557 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 16/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.164742, err = 0.037800\n",
      "\u001b[0m\n",
      "it 47/200, Jtr_pred = 0.061796, err = 0.020250, \u001b[31m   time: 4.359384 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.168205, err = 0.040500\n",
      "\u001b[0m\n",
      "it 48/200, Jtr_pred = 0.056368, err = 0.019450, \u001b[31m   time: 4.439556 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 17/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.147534, err = 0.035300\n",
      "\u001b[0m\n",
      "it 49/200, Jtr_pred = 0.060489, err = 0.020800, \u001b[31m   time: 4.509634 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.158681, err = 0.036500\n",
      "\u001b[0m\n",
      "it 50/200, Jtr_pred = 0.059656, err = 0.019517, \u001b[31m   time: 4.596516 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 18/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162543, err = 0.041300\n",
      "\u001b[0m\n",
      "it 51/200, Jtr_pred = 0.061185, err = 0.019417, \u001b[31m   time: 4.483892 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.167900, err = 0.039700\n",
      "\u001b[0m\n",
      "it 52/200, Jtr_pred = 0.058194, err = 0.019700, \u001b[31m   time: 4.387217 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 19/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.183642, err = 0.045000\n",
      "\u001b[0m\n",
      "it 53/200, Jtr_pred = 0.058761, err = 0.019150, \u001b[31m   time: 4.396444 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.162514, err = 0.038400\n",
      "\u001b[0m\n",
      "it 54/200, Jtr_pred = 0.059737, err = 0.020033, \u001b[31m   time: 4.572475 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 20/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.153479, err = 0.036000\n",
      "\u001b[0m\n",
      "it 55/200, Jtr_pred = 0.059429, err = 0.019700, \u001b[31m   time: 4.731289 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.158700, err = 0.035200\n",
      "\u001b[0m\n",
      "it 56/200, Jtr_pred = 0.059508, err = 0.020100, \u001b[31m   time: 4.666043 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 21/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.145098, err = 0.034100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 57/200, Jtr_pred = 0.056862, err = 0.018733, \u001b[31m   time: 4.564637 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.148354, err = 0.033800\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 58/200, Jtr_pred = 0.059630, err = 0.019933, \u001b[31m   time: 4.401630 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 22/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.154365, err = 0.034500\n",
      "\u001b[0m\n",
      "it 59/200, Jtr_pred = 0.054708, err = 0.018300, \u001b[31m   time: 3.984603 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.159006, err = 0.037900\n",
      "\u001b[0m\n",
      "it 60/200, Jtr_pred = 0.055883, err = 0.018567, \u001b[31m   time: 3.813604 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 23/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.165267, err = 0.038800\n",
      "\u001b[0m\n",
      "it 61/200, Jtr_pred = 0.056568, err = 0.019133, \u001b[31m   time: 3.563351 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.168801, err = 0.039600\n",
      "\u001b[0m\n",
      "it 62/200, Jtr_pred = 0.058612, err = 0.019450, \u001b[31m   time: 3.857494 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 24/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171926, err = 0.038000\n",
      "\u001b[0m\n",
      "it 63/200, Jtr_pred = 0.056217, err = 0.018683, \u001b[31m   time: 3.960485 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.172165, err = 0.042000\n",
      "\u001b[0m\n",
      "it 64/200, Jtr_pred = 0.054587, err = 0.019100, \u001b[31m   time: 3.651268 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 25/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.164109, err = 0.036600\n",
      "\u001b[0m\n",
      "it 65/200, Jtr_pred = 0.058861, err = 0.019183, \u001b[31m   time: 3.965361 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.172688, err = 0.038400\n",
      "\u001b[0m\n",
      "it 66/200, Jtr_pred = 0.056244, err = 0.018500, \u001b[31m   time: 6.528364 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 26/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177434, err = 0.040400\n",
      "\u001b[0m\n",
      "it 67/200, Jtr_pred = 0.055859, err = 0.018767, \u001b[31m   time: 5.580965 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169986, err = 0.037300\n",
      "\u001b[0m\n",
      "it 68/200, Jtr_pred = 0.055205, err = 0.018367, \u001b[31m   time: 3.535367 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 27/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.167400, err = 0.039000\n",
      "\u001b[0m\n",
      "it 69/200, Jtr_pred = 0.058686, err = 0.019017, \u001b[31m   time: 3.990341 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.165202, err = 0.037700\n",
      "\u001b[0m\n",
      "it 70/200, Jtr_pred = 0.057589, err = 0.019833, \u001b[31m   time: 3.817229 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 28/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190003, err = 0.040100\n",
      "\u001b[0m\n",
      "it 71/200, Jtr_pred = 0.058009, err = 0.019433, \u001b[31m   time: 3.981579 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173398, err = 0.037700\n",
      "\u001b[0m\n",
      "it 72/200, Jtr_pred = 0.054491, err = 0.018267, \u001b[31m   time: 3.649690 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 29/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173829, err = 0.037900\n",
      "\u001b[0m\n",
      "it 73/200, Jtr_pred = 0.056272, err = 0.018217, \u001b[31m   time: 3.512995 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.172869, err = 0.037500\n",
      "\u001b[0m\n",
      "it 74/200, Jtr_pred = 0.055955, err = 0.018133, \u001b[31m   time: 3.495530 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 30/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.161553, err = 0.035400\n",
      "\u001b[0m\n",
      "it 75/200, Jtr_pred = 0.054725, err = 0.018167, \u001b[31m   time: 3.555295 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179956, err = 0.037200\n",
      "\u001b[0m\n",
      "it 76/200, Jtr_pred = 0.056674, err = 0.018633, \u001b[31m   time: 3.434933 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 31/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.189550, err = 0.039600\n",
      "\u001b[0m\n",
      "it 77/200, Jtr_pred = 0.053816, err = 0.017650, \u001b[31m   time: 3.556928 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.166582, err = 0.035500\n",
      "\u001b[0m\n",
      "it 78/200, Jtr_pred = 0.055393, err = 0.018150, \u001b[31m   time: 3.658474 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 32/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177641, err = 0.038200\n",
      "\u001b[0m\n",
      "it 79/200, Jtr_pred = 0.053495, err = 0.017217, \u001b[31m   time: 3.628151 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169736, err = 0.036900\n",
      "\u001b[0m\n",
      "it 80/200, Jtr_pred = 0.054585, err = 0.017900, \u001b[31m   time: 4.063931 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 33/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.174369, err = 0.035400\n",
      "\u001b[0m\n",
      "it 81/200, Jtr_pred = 0.053983, err = 0.017950, \u001b[31m   time: 3.523016 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.167384, err = 0.035800\n",
      "\u001b[0m\n",
      "it 82/200, Jtr_pred = 0.053864, err = 0.017517, \u001b[31m   time: 3.947497 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 34/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169657, err = 0.036300\n",
      "\u001b[0m\n",
      "it 83/200, Jtr_pred = 0.052780, err = 0.017983, \u001b[31m   time: 3.998291 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177064, err = 0.038900\n",
      "\u001b[0m\n",
      "it 84/200, Jtr_pred = 0.056420, err = 0.019200, \u001b[31m   time: 3.892065 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 35/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171299, err = 0.036600\n",
      "\u001b[0m\n",
      "it 85/200, Jtr_pred = 0.054221, err = 0.017333, \u001b[31m   time: 3.946537 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.186461, err = 0.038600\n",
      "\u001b[0m\n",
      "it 86/200, Jtr_pred = 0.050698, err = 0.017283, \u001b[31m   time: 3.995486 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 36/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173887, err = 0.036300\n",
      "\u001b[0m\n",
      "it 87/200, Jtr_pred = 0.053626, err = 0.017233, \u001b[31m   time: 3.945550 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169970, err = 0.037700\n",
      "\u001b[0m\n",
      "it 88/200, Jtr_pred = 0.055797, err = 0.017717, \u001b[31m   time: 3.666245 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 37/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.164874, err = 0.035700\n",
      "\u001b[0m\n",
      "it 89/200, Jtr_pred = 0.055111, err = 0.018333, \u001b[31m   time: 4.029143 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.187527, err = 0.039800\n",
      "\u001b[0m\n",
      "it 90/200, Jtr_pred = 0.053397, err = 0.018033, \u001b[31m   time: 4.246356 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 38/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.230010, err = 0.047600\n",
      "\u001b[0m\n",
      "it 91/200, Jtr_pred = 0.054030, err = 0.018017, \u001b[31m   time: 3.482121 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.179693, err = 0.038700\n",
      "\u001b[0m\n",
      "it 92/200, Jtr_pred = 0.052203, err = 0.017083, \u001b[31m   time: 3.445323 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 39/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.164556, err = 0.033100\n",
      "\u001b[0m\n",
      "\u001b[34mbest test error\u001b[0m\n",
      "it 93/200, Jtr_pred = 0.053025, err = 0.017417, \u001b[31m   time: 4.017099 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171268, err = 0.038100\n",
      "\u001b[0m\n",
      "it 94/200, Jtr_pred = 0.052763, err = 0.017600, \u001b[31m   time: 3.544143 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 40/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.176850, err = 0.037500\n",
      "\u001b[0m\n",
      "it 95/200, Jtr_pred = 0.053637, err = 0.017500, \u001b[31m   time: 4.099721 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177633, err = 0.037300\n",
      "\u001b[0m\n",
      "it 96/200, Jtr_pred = 0.052131, err = 0.017450, \u001b[31m   time: 3.766685 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 41/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169403, err = 0.035700\n",
      "\u001b[0m\n",
      "it 97/200, Jtr_pred = 0.052450, err = 0.017200, \u001b[31m   time: 3.892391 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.193458, err = 0.041200\n",
      "\u001b[0m\n",
      "it 98/200, Jtr_pred = 0.051995, err = 0.017267, \u001b[31m   time: 4.008811 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 42/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.186983, err = 0.038300\n",
      "\u001b[0m\n",
      "it 99/200, Jtr_pred = 0.055379, err = 0.017617, \u001b[31m   time: 3.863725 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190045, err = 0.038800\n",
      "\u001b[0m\n",
      "it 100/200, Jtr_pred = 0.053462, err = 0.017883, \u001b[31m   time: 3.647043 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 43/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191827, err = 0.037300\n",
      "\u001b[0m\n",
      "it 101/200, Jtr_pred = 0.050162, err = 0.017217, \u001b[31m   time: 3.597516 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.178336, err = 0.037100\n",
      "\u001b[0m\n",
      "it 102/200, Jtr_pred = 0.051322, err = 0.017267, \u001b[31m   time: 3.642589 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 44/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208571, err = 0.041300\n",
      "\u001b[0m\n",
      "it 103/200, Jtr_pred = 0.051888, err = 0.017200, \u001b[31m   time: 3.501869 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.165651, err = 0.034100\n",
      "\u001b[0m\n",
      "it 104/200, Jtr_pred = 0.050702, err = 0.016400, \u001b[31m   time: 4.057427 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 45/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.171806, err = 0.034500\n",
      "\u001b[0m\n",
      "it 105/200, Jtr_pred = 0.052186, err = 0.017100, \u001b[31m   time: 3.607008 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.198377, err = 0.038500\n",
      "\u001b[0m\n",
      "it 106/200, Jtr_pred = 0.051275, err = 0.016717, \u001b[31m   time: 3.622025 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 46/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.183333, err = 0.037200\n",
      "\u001b[0m\n",
      "it 107/200, Jtr_pred = 0.051952, err = 0.016117, \u001b[31m   time: 3.555393 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.181700, err = 0.038300\n",
      "\u001b[0m\n",
      "it 108/200, Jtr_pred = 0.050560, err = 0.016600, \u001b[31m   time: 3.589240 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 47/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.185344, err = 0.039100\n",
      "\u001b[0m\n",
      "it 109/200, Jtr_pred = 0.052512, err = 0.017667, \u001b[31m   time: 3.496199 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.169714, err = 0.037100\n",
      "\u001b[0m\n",
      "it 110/200, Jtr_pred = 0.053964, err = 0.017383, \u001b[31m   time: 3.708411 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36m saving weight samples 48/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.177414, err = 0.035700\n",
      "\u001b[0m\n",
      "it 111/200, Jtr_pred = 0.054672, err = 0.017417, \u001b[31m   time: 3.586672 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.199219, err = 0.042100\n",
      "\u001b[0m\n",
      "it 112/200, Jtr_pred = 0.052746, err = 0.016300, \u001b[31m   time: 3.572512 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 49/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.170534, err = 0.033300\n",
      "\u001b[0m\n",
      "it 113/200, Jtr_pred = 0.054636, err = 0.017267, \u001b[31m   time: 3.615088 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.173609, err = 0.036000\n",
      "\u001b[0m\n",
      "it 114/200, Jtr_pred = 0.051176, err = 0.016450, \u001b[31m   time: 3.659357 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 50/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.184525, err = 0.036700\n",
      "\u001b[0m\n",
      "it 115/200, Jtr_pred = 0.051435, err = 0.016433, \u001b[31m   time: 3.981005 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.182783, err = 0.035700\n",
      "\u001b[0m\n",
      "it 116/200, Jtr_pred = 0.052500, err = 0.017367, \u001b[31m   time: 3.855938 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 51/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191872, err = 0.039600\n",
      "\u001b[0m\n",
      "it 117/200, Jtr_pred = 0.053907, err = 0.017517, \u001b[31m   time: 3.946807 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.180990, err = 0.036100\n",
      "\u001b[0m\n",
      "it 118/200, Jtr_pred = 0.053834, err = 0.017317, \u001b[31m   time: 4.026427 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 52/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.187608, err = 0.035100\n",
      "\u001b[0m\n",
      "it 119/200, Jtr_pred = 0.052146, err = 0.017050, \u001b[31m   time: 4.057083 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.189330, err = 0.037500\n",
      "\u001b[0m\n",
      "it 120/200, Jtr_pred = 0.051371, err = 0.016850, \u001b[31m   time: 3.647574 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 53/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201952, err = 0.040200\n",
      "\u001b[0m\n",
      "it 121/200, Jtr_pred = 0.049370, err = 0.015833, \u001b[31m   time: 3.957598 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.182039, err = 0.035700\n",
      "\u001b[0m\n",
      "it 122/200, Jtr_pred = 0.049760, err = 0.016117, \u001b[31m   time: 3.875811 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 54/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190567, err = 0.037200\n",
      "\u001b[0m\n",
      "it 123/200, Jtr_pred = 0.052161, err = 0.017300, \u001b[31m   time: 3.921535 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.193367, err = 0.038300\n",
      "\u001b[0m\n",
      "it 124/200, Jtr_pred = 0.051373, err = 0.016800, \u001b[31m   time: 3.496533 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 55/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.190701, err = 0.038000\n",
      "\u001b[0m\n",
      "it 125/200, Jtr_pred = 0.050697, err = 0.016100, \u001b[31m   time: 3.517247 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191522, err = 0.037300\n",
      "\u001b[0m\n",
      "it 126/200, Jtr_pred = 0.051739, err = 0.016650, \u001b[31m   time: 3.549643 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 56/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.196901, err = 0.037900\n",
      "\u001b[0m\n",
      "it 127/200, Jtr_pred = 0.052887, err = 0.017200, \u001b[31m   time: 3.947002 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.203827, err = 0.038700\n",
      "\u001b[0m\n",
      "it 128/200, Jtr_pred = 0.053761, err = 0.017383, \u001b[31m   time: 3.788620 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 57/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.180420, err = 0.036700\n",
      "\u001b[0m\n",
      "it 129/200, Jtr_pred = 0.053267, err = 0.016650, \u001b[31m   time: 4.006123 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.191335, err = 0.036300\n",
      "\u001b[0m\n",
      "it 130/200, Jtr_pred = 0.050839, err = 0.016367, \u001b[31m   time: 3.972763 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 58/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.216737, err = 0.039900\n",
      "\u001b[0m\n",
      "it 131/200, Jtr_pred = 0.051977, err = 0.016883, \u001b[31m   time: 4.033890 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208405, err = 0.037600\n",
      "\u001b[0m\n",
      "it 132/200, Jtr_pred = 0.051418, err = 0.016433, \u001b[31m   time: 3.866222 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 59/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.200667, err = 0.037300\n",
      "\u001b[0m\n",
      "it 133/200, Jtr_pred = 0.052673, err = 0.016867, \u001b[31m   time: 4.002170 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.195615, err = 0.036200\n",
      "\u001b[0m\n",
      "it 134/200, Jtr_pred = 0.051107, err = 0.016767, \u001b[31m   time: 3.502293 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 60/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.203546, err = 0.037300\n",
      "\u001b[0m\n",
      "it 135/200, Jtr_pred = 0.050715, err = 0.016617, \u001b[31m   time: 3.817715 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.207089, err = 0.039400\n",
      "\u001b[0m\n",
      "it 136/200, Jtr_pred = 0.050915, err = 0.015817, \u001b[31m   time: 3.439597 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 61/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208215, err = 0.036100\n",
      "\u001b[0m\n",
      "it 137/200, Jtr_pred = 0.053688, err = 0.015950, \u001b[31m   time: 3.745930 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.200133, err = 0.036100\n",
      "\u001b[0m\n",
      "it 138/200, Jtr_pred = 0.051031, err = 0.016117, \u001b[31m   time: 3.866534 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 62/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.204751, err = 0.036300\n",
      "\u001b[0m\n",
      "it 139/200, Jtr_pred = 0.052317, err = 0.016500, \u001b[31m   time: 3.582053 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.220652, err = 0.040400\n",
      "\u001b[0m\n",
      "it 140/200, Jtr_pred = 0.048794, err = 0.016033, \u001b[31m   time: 3.548695 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 63/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.205979, err = 0.039600\n",
      "\u001b[0m\n",
      "it 141/200, Jtr_pred = 0.049476, err = 0.015183, \u001b[31m   time: 3.522458 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.248329, err = 0.043900\n",
      "\u001b[0m\n",
      "it 142/200, Jtr_pred = 0.051567, err = 0.016400, \u001b[31m   time: 3.639990 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 64/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.192038, err = 0.034700\n",
      "\u001b[0m\n",
      "it 143/200, Jtr_pred = 0.049684, err = 0.015750, \u001b[31m   time: 3.736028 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208896, err = 0.039700\n",
      "\u001b[0m\n",
      "it 144/200, Jtr_pred = 0.048546, err = 0.015333, \u001b[31m   time: 4.070613 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 65/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.192267, err = 0.036500\n",
      "\u001b[0m\n",
      "it 145/200, Jtr_pred = 0.050757, err = 0.016667, \u001b[31m   time: 3.633661 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.206307, err = 0.038000\n",
      "\u001b[0m\n",
      "it 146/200, Jtr_pred = 0.050417, err = 0.015767, \u001b[31m   time: 4.129116 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 66/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.209961, err = 0.040000\n",
      "\u001b[0m\n",
      "it 147/200, Jtr_pred = 0.047393, err = 0.015367, \u001b[31m   time: 3.986576 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.198454, err = 0.037300\n",
      "\u001b[0m\n",
      "it 148/200, Jtr_pred = 0.048075, err = 0.015117, \u001b[31m   time: 4.050663 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 67/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.202949, err = 0.039000\n",
      "\u001b[0m\n",
      "it 149/200, Jtr_pred = 0.050219, err = 0.015717, \u001b[31m   time: 3.908916 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.205692, err = 0.038300\n",
      "\u001b[0m\n",
      "it 150/200, Jtr_pred = 0.049746, err = 0.015983, \u001b[31m   time: 3.691412 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 68/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.221765, err = 0.042200\n",
      "\u001b[0m\n",
      "it 151/200, Jtr_pred = 0.051900, err = 0.015950, \u001b[31m   time: 3.742510 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.188484, err = 0.034600\n",
      "\u001b[0m\n",
      "it 152/200, Jtr_pred = 0.048560, err = 0.015400, \u001b[31m   time: 3.801969 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 69/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208318, err = 0.037300\n",
      "\u001b[0m\n",
      "it 153/200, Jtr_pred = 0.051908, err = 0.016400, \u001b[31m   time: 3.884933 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.187277, err = 0.034300\n",
      "\u001b[0m\n",
      "it 154/200, Jtr_pred = 0.051788, err = 0.016583, \u001b[31m   time: 4.007143 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 70/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.217140, err = 0.039600\n",
      "\u001b[0m\n",
      "it 155/200, Jtr_pred = 0.050470, err = 0.015533, \u001b[31m   time: 3.497630 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201787, err = 0.037800\n",
      "\u001b[0m\n",
      "it 156/200, Jtr_pred = 0.049081, err = 0.015350, \u001b[31m   time: 3.913645 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 71/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.200556, err = 0.037000\n",
      "\u001b[0m\n",
      "it 157/200, Jtr_pred = 0.050538, err = 0.015767, \u001b[31m   time: 3.940514 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.217453, err = 0.039200\n",
      "\u001b[0m\n",
      "it 158/200, Jtr_pred = 0.049450, err = 0.015900, \u001b[31m   time: 4.006599 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 72/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208735, err = 0.038900\n",
      "\u001b[0m\n",
      "it 159/200, Jtr_pred = 0.049544, err = 0.015550, \u001b[31m   time: 3.959642 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.214211, err = 0.037300\n",
      "\u001b[0m\n",
      "it 160/200, Jtr_pred = 0.048873, err = 0.015117, \u001b[31m   time: 3.802093 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 73/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.228304, err = 0.040100\n",
      "\u001b[0m\n",
      "it 161/200, Jtr_pred = 0.052655, err = 0.016233, \u001b[31m   time: 4.046552 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.225481, err = 0.040000\n",
      "\u001b[0m\n",
      "it 162/200, Jtr_pred = 0.049815, err = 0.016050, \u001b[31m   time: 3.661706 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 74/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.214060, err = 0.038500\n",
      "\u001b[0m\n",
      "it 163/200, Jtr_pred = 0.050991, err = 0.016383, \u001b[31m   time: 3.529930 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.225069, err = 0.038300\n",
      "\u001b[0m\n",
      "it 164/200, Jtr_pred = 0.053059, err = 0.016917, \u001b[31m   time: 3.535531 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 75/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.229182, err = 0.039500\n",
      "\u001b[0m\n",
      "it 165/200, Jtr_pred = 0.048545, err = 0.015183, \u001b[31m   time: 3.925636 seconds\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32m    Jdev = 0.206668, err = 0.035700\n",
      "\u001b[0m\n",
      "it 166/200, Jtr_pred = 0.049394, err = 0.015833, \u001b[31m   time: 3.932917 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 76/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.204366, err = 0.036900\n",
      "\u001b[0m\n",
      "it 167/200, Jtr_pred = 0.046792, err = 0.015450, \u001b[31m   time: 4.046823 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.227277, err = 0.043200\n",
      "\u001b[0m\n",
      "it 168/200, Jtr_pred = 0.053741, err = 0.016400, \u001b[31m   time: 3.786335 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 77/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.225985, err = 0.044300\n",
      "\u001b[0m\n",
      "it 169/200, Jtr_pred = 0.051845, err = 0.015817, \u001b[31m   time: 3.585082 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.200756, err = 0.035400\n",
      "\u001b[0m\n",
      "it 170/200, Jtr_pred = 0.052295, err = 0.016833, \u001b[31m   time: 3.780744 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 78/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.218139, err = 0.038900\n",
      "\u001b[0m\n",
      "it 171/200, Jtr_pred = 0.052200, err = 0.016583, \u001b[31m   time: 3.943008 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.219118, err = 0.035900\n",
      "\u001b[0m\n",
      "it 172/200, Jtr_pred = 0.051646, err = 0.015950, \u001b[31m   time: 3.866087 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 79/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.223533, err = 0.037400\n",
      "\u001b[0m\n",
      "it 173/200, Jtr_pred = 0.051809, err = 0.016633, \u001b[31m   time: 4.054900 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.215998, err = 0.036800\n",
      "\u001b[0m\n",
      "it 174/200, Jtr_pred = 0.049726, err = 0.015900, \u001b[31m   time: 7.168604 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 80/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.225757, err = 0.039200\n",
      "\u001b[0m\n",
      "it 175/200, Jtr_pred = 0.050545, err = 0.015483, \u001b[31m   time: 3.764562 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.251580, err = 0.042100\n",
      "\u001b[0m\n",
      "it 176/200, Jtr_pred = 0.050107, err = 0.015867, \u001b[31m   time: 3.950578 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 81/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.218119, err = 0.038800\n",
      "\u001b[0m\n",
      "it 177/200, Jtr_pred = 0.048951, err = 0.015200, \u001b[31m   time: 3.828080 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.220905, err = 0.039100\n",
      "\u001b[0m\n",
      "it 178/200, Jtr_pred = 0.052347, err = 0.015800, \u001b[31m   time: 3.539571 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 82/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.214738, err = 0.037000\n",
      "\u001b[0m\n",
      "it 179/200, Jtr_pred = 0.050695, err = 0.016067, \u001b[31m   time: 4.048931 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.205046, err = 0.038500\n",
      "\u001b[0m\n",
      "it 180/200, Jtr_pred = 0.051589, err = 0.016833, \u001b[31m   time: 4.052448 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 83/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.227649, err = 0.039800\n",
      "\u001b[0m\n",
      "it 181/200, Jtr_pred = 0.050604, err = 0.015850, \u001b[31m   time: 3.469606 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.207896, err = 0.040300\n",
      "\u001b[0m\n",
      "it 182/200, Jtr_pred = 0.050358, err = 0.016033, \u001b[31m   time: 3.982930 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 84/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201044, err = 0.036000\n",
      "\u001b[0m\n",
      "it 183/200, Jtr_pred = 0.047611, err = 0.014533, \u001b[31m   time: 4.004660 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.202390, err = 0.038000\n",
      "\u001b[0m\n",
      "it 184/200, Jtr_pred = 0.048468, err = 0.015700, \u001b[31m   time: 3.774549 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 85/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.223357, err = 0.040000\n",
      "\u001b[0m\n",
      "it 185/200, Jtr_pred = 0.049025, err = 0.015467, \u001b[31m   time: 3.853853 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.206984, err = 0.036600\n",
      "\u001b[0m\n",
      "it 186/200, Jtr_pred = 0.048861, err = 0.015083, \u001b[31m   time: 3.447003 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 86/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.212856, err = 0.039400\n",
      "\u001b[0m\n",
      "it 187/200, Jtr_pred = 0.049916, err = 0.015100, \u001b[31m   time: 3.973235 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.217180, err = 0.037200\n",
      "\u001b[0m\n",
      "it 188/200, Jtr_pred = 0.048804, err = 0.015417, \u001b[31m   time: 3.620623 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 87/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.218431, err = 0.038500\n",
      "\u001b[0m\n",
      "it 189/200, Jtr_pred = 0.048959, err = 0.015433, \u001b[31m   time: 4.157688 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.221074, err = 0.036400\n",
      "\u001b[0m\n",
      "it 190/200, Jtr_pred = 0.050841, err = 0.015483, \u001b[31m   time: 3.483115 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 88/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.214844, err = 0.038300\n",
      "\u001b[0m\n",
      "it 191/200, Jtr_pred = 0.049327, err = 0.015750, \u001b[31m   time: 3.557545 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.208623, err = 0.036400\n",
      "\u001b[0m\n",
      "it 192/200, Jtr_pred = 0.048156, err = 0.015617, \u001b[31m   time: 3.418516 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 89/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.209163, err = 0.036100\n",
      "\u001b[0m\n",
      "it 193/200, Jtr_pred = 0.049058, err = 0.015200, \u001b[31m   time: 3.658337 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.204265, err = 0.037000\n",
      "\u001b[0m\n",
      "it 194/200, Jtr_pred = 0.047987, err = 0.014917, \u001b[31m   time: 3.448466 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 90/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.201024, err = 0.035700\n",
      "\u001b[0m\n",
      "it 195/200, Jtr_pred = 0.049598, err = 0.015633, \u001b[31m   time: 3.984477 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.222687, err = 0.037700\n",
      "\u001b[0m\n",
      "it 196/200, Jtr_pred = 0.049861, err = 0.015950, \u001b[31m   time: 3.962825 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 90/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.203776, err = 0.035400\n",
      "\u001b[0m\n",
      "it 197/200, Jtr_pred = 0.049478, err = 0.016050, \u001b[31m   time: 4.003312 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.183301, err = 0.036500\n",
      "\u001b[0m\n",
      "it 198/200, Jtr_pred = 0.044731, err = 0.014667, \u001b[31m   time: 3.511778 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m saving weight samples 90/90\u001b[0m\n",
      "\u001b[32m    Jdev = 0.198342, err = 0.040100\n",
      "\u001b[0m\n",
      "it 199/200, Jtr_pred = 0.047623, err = 0.014667, \u001b[31m   time: 3.548879 seconds\n",
      "\u001b[0m\n",
      "\u001b[32m    Jdev = 0.189461, err = 0.036400\n",
      "\u001b[0m\n",
      "\u001b[31m   average time: 4.808156 seconds\n",
      "\u001b[0m\n",
      "\u001b[36m\n",
      "RESULTS:\u001b[0m\n",
      "  cost_dev: 0.122493 (cost_train 0.044731)\n",
      "  err_dev: 0.033100\n",
      "  nb_parameters: 2395210 (2.28MB)\n",
      "  time_per_it: 4.808156s\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_pSGLD_MNIST'\n",
    "results_dir = 'results_pSGLD_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 128\n",
    "nb_epochs = 200 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-4 # 1e-5 for sgld\n",
    "prior_sig = 0.1\n",
    "########################################################################################\n",
    "net = Net_langevin(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, N_train=NTrainPointsMNIST, prior_sig=prior_sig)\n",
    "\n",
    "epoch = 0\n",
    "\n",
    "## weight saving parameters #######\n",
    "start_save = 15\n",
    "save_every = 2 # we can save every 40 as \n",
    "N_saves = 90\n",
    "###################################\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# train\n",
    "cprint('c', '\\nTrain:')\n",
    "\n",
    "print('  init cost variables:')\n",
    "pred_cost_train = np.zeros(nb_epochs)\n",
    "err_train = np.zeros(nb_epochs)\n",
    "\n",
    "cost_dev = np.zeros(nb_epochs)\n",
    "err_dev = np.zeros(nb_epochs)\n",
    "# best_cost = np.inf\n",
    "best_err = np.inf\n",
    "\n",
    "\n",
    "nb_its_dev = 1\n",
    "\n",
    "tic0 = time.time()\n",
    "for i in range(epoch, nb_epochs):\n",
    "    \n",
    "#     if i in [1]:\n",
    "#         print('updating lr')\n",
    "#         net.sched.step()\n",
    "    \n",
    "    net.set_mode_train(True)\n",
    "\n",
    "    tic = time.time()\n",
    "    nb_samples = 0\n",
    "\n",
    "    for x, y in trainloader:\n",
    "        cost_pred, err = net.fit(x, y)\n",
    "\n",
    "        err_train[i] += err\n",
    "        pred_cost_train[i] += cost_pred\n",
    "        nb_samples += len(x)\n",
    "\n",
    "    pred_cost_train[i] /= nb_samples\n",
    "    err_train[i] /= nb_samples\n",
    "\n",
    "    toc = time.time()\n",
    "    net.epoch = i\n",
    "    # ---- print\n",
    "    print(\"it %d/%d, Jtr_pred = %f, err = %f, \" % (i, nb_epochs, pred_cost_train[i], err_train[i]), end=\"\")\n",
    "    cprint('r', '   time: %f seconds\\n' % (toc - tic))\n",
    "    \n",
    "    # ---- save weights\n",
    "    if i >= start_save and i % save_every == 0:\n",
    "        net.save_sampled_net(max_samples=N_saves)\n",
    "\n",
    "    # ---- dev\n",
    "    if i % nb_its_dev == 0:\n",
    "        net.set_mode_train(False)\n",
    "        nb_samples = 0\n",
    "        for j, (x, y) in enumerate(valloader):\n",
    "\n",
    "            cost, err, probs = net.eval(x, y)\n",
    "\n",
    "            cost_dev[i] += cost\n",
    "            err_dev[i] += err\n",
    "            nb_samples += len(x)\n",
    "\n",
    "        cost_dev[i] /= nb_samples\n",
    "        err_dev[i] /= nb_samples\n",
    "\n",
    "        cprint('g', '    Jdev = %f, err = %f\\n' % (cost_dev[i], err_dev[i]))\n",
    "\n",
    "        if err_dev[i] < best_err:\n",
    "            best_err = err_dev[i]\n",
    "            cprint('b', 'best test error')\n",
    "#             net.save(models_dir+'/theta_best.dat')\n",
    "\n",
    "toc0 = time.time()\n",
    "runtime_per_it = (toc0 - tic0) / float(nb_epochs)\n",
    "cprint('r', '   average time: %f seconds\\n' % runtime_per_it)\n",
    "\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# results\n",
    "cprint('c', '\\nRESULTS:')\n",
    "nb_parameters = net.get_nb_parameters()\n",
    "best_cost_dev = np.min(cost_dev)\n",
    "best_cost_train = np.min(pred_cost_train)\n",
    "err_dev_min = err_dev[::nb_its_dev].min()\n",
    "\n",
    "print('  cost_dev: %f (cost_train %f)' % (best_cost_dev, best_cost_train))\n",
    "print('  err_dev: %f' % (err_dev_min))\n",
    "print('  nb_parameters: %d (%s)' % (nb_parameters, humansize(nb_parameters)))\n",
    "print('  time_per_it: %fs\\n' % (runtime_per_it))\n",
    "\n",
    "\n",
    "\n",
    "## Save results for plots\n",
    "# np.save('results/test_predictions.npy', test_predictions)\n",
    "np.save(results_dir + '/cost_train.npy', pred_cost_train)\n",
    "np.save(results_dir + '/cost_dev.npy', cost_dev)\n",
    "np.save(results_dir + '/err_train.npy', err_train)\n",
    "np.save(results_dir + '/err_dev.npy', err_dev)\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# fig cost vs its\n",
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), box_inches='tight')\n",
    "\n",
    " \n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qkCTpSuBtSZKiiQcLZFmu64329qVhi3lAAoFAcDAGtAICkGX5FeCVQ9GWqIQgEAgEnWegu+AOKTq9nmA4LCwggUAg6ARCAfUwf9yyEecJJ/a1GAKBQNDvEQqoh9njcWPswuxogUAgOFIRCqiHOSY5lZZWE78EAoFA0D5CAfUwZ2fn0LTi274WQyAQCPo9AzoLTpKkPKD1kn9OwCHLcq+VhA0oYVENWyAQCDrBgLaAZFneLctyYfQPVRm92pttBsJhkYYtEPQzVq36gSVL3u/Rc65Zs4pZs6awc2dRj573SGJAW0CtkSTJDPwaOKU32wkoiliQTiDoZ6xe/QPhcJj583/ZY+eUpFE8/fS/YhaCE3SNAW0B7cdpQKksy726ZnAgLFxwAsHhSnSV0s5gs9kZO3YcFou1l6XqGJ/P18F2b7fPGQqFCB6iuYxHjAUEXAK80Nmdu7scQ8WuIuaNHs2rrUrdi+UYxHIMYjmGvluO4U9/upE1a1YBakVogPPO+w3HHXciDz10P3Z7AjNnzuadd96gvLyM++77P/x+H598soSiou00NNSTkpLKrFnHcsop8xg1agxVVZV8880yHn74AW6//R6OOeZY7Zmcc84FNDU1sWLFMvR6AxMnTubMM8/FZDK1e02bNm0kP19i1aofePbZJ9m9exdms4mZM2dz441/YNeuXQCsWLGcl156nmeffZFHHvkbRUXbmDfvF0yffjS33noTN9xwCytWLGfdup8YMmQojzzyBCaTmb///W98++1ympubyMzM4pJLLmfEiBHac3r55eepqanhV786k5dffoHKykpuuOFmbZE+sRzDz0SSpGxgO5Ary3LtQXbPo5vVsEFUUO4O/VU2IZfK4V4Nu7S0hNtv/yOKEuaGG/4AQHp6OunpGSxceBl79+4hOTmFiy66BKvVyvjxhWzZspl1635CkkYTHx9PUdE2nn/+GX7zm4u58MIFgBoDuuaaK3j55dcZNkxdmmHWrCmkp2cwefJUTjzxFHbs2M4zz/yDyy77Pb/+9f+0kS16z9avX8u1117JMcfMYd68U2lsbOTpp59g7Nhx2rIMS5a8z3333UV29iB+9auzGDVqNHa7nYQEB2effRopKSnMnn08s2cfi15vYPLkqTzzzD949dWXWbDgUkaPLuCrr77k/fff5Y477uGkk+YCcO+9d7JixXISEhJZsOB3JCcn4/P5mTVrdht5e7oa9pFiAV0MfNgJ5fOzmZTopGHplziP61/LMQgERyqDBuVgs9kIh8OMHTuuzffNzS5eeOEVUlJStW1TpkxjypRpgLqGz/jxhXi9Xt57b7GmgDoiKyubW2+9E4Dp02ewYcM6vv56absKKIqqbMZz9933a9vS0tK59tor2bmzSFNwAGeddR7nnHO+9rm8vAyAMWPG8b//e4u2vampkbfeeo3/+Z/fcvHFv9Pkqa6u5IUXntUUEKgL1f39708ycqRq9Ww6RHMZjyQFdM2haGhGUgp1H38oFJBgwLP3wftjPhvcLTRUHYfzuBMI+3yUPvpwm2McM2eROPMYQs3NlD31RJvvnXOOJ2HadAJ1tVT889k23yedPBd74cSeuwjUZILWygfU2Mp//vMin376EZWVFTExkWAwiNHYcdc5der0mM95ecPYunVLh/t7vV42bdrAddfdFNPO+PGFGI1GZHlrjAI6+uhZ7Z5n/+07d+7A6/Vy3HGxpcFOOOFk7r33Turr60hKUmekpKWla8rnUHJEKCBZlvMPVVt+RaRhCwSHE8nJbacFPvXU43zwwWIWLLiU/PxRJCQksHz517z00vP4/f4DKqCEhISYz0ajEb+//WQBgObmJkKhEA899Fceeuivbb6vrKw4qLyApkyi1NbWtLt/dL/m5ibt//sfe6g4IhTQoSQgqmELjhAG3/zHmM+bNm3EGYkB6S2WNt+3xpCQcMDvTckpB/y+J9HpdG22LV36OWeeeW6M22zFim96pX27PQGdTscll1zGjBkz23yfmpq235a28kLb64hadfX19SS2qk9ZX68uh5aQkNjhsYcKoYB6mICiCAUkEPQzDAYjXq+r0/v7fD7MZrP2ORQK8cUXn/aGaMTFxVFQMI7i4j0sWHBpj5132LDhWK1Wli79POa8X375GYMH52qZi32JUEA9TLQSgqIofTaqEAgEsWRmZvHZZx+xbNlXpKenk5qa1o5lsY+pU6ezaNGbDBqUg8ORyKJFb+L3955r/corr+G6665Er9cxZ84JxMfbqKysYMWKb7jsst+Tm3vwLMT9cTgSOfvs83nppecxGAyMGjWGr7/+kpUrv+XOO+/thavoOkIB9TD/rSjjljcXH3xHgUBwyJgz53gaGuq5//67aW5uYsGCS/ntby/vcP/rr7+Jv/3tfh5++EEsFgvz5p3K7NnH8eCDvdNxT5hQyBNPPMfzzz/DX/5yB+FwiMzMLKZPn0Fyckq3z/u7312B0Whk8eJ3qKt7lpycwdx++1848cReLQjTaY6IeUBdJA8xD+iQ0l9lE3KpHO7zgKD/yna4ydXT84COpFI8h4ThNhvVb79J2Ovpa1EEAoGgXyMUUA9SXtvCqKyR1H+8hJBbKCCBQCA4EEIB9SBfry3DOfwYAJEJJxAIBAdhwCchSJJkBR4BTgS8wEpZli/rjbasZgOKMQ5ATEYVCASCgzDgFRDwIKriyZdlWZEkKaO3GrJZTQT1BgCxJINAIBAchAGtgCRJsgMXATmyLCsAsixX9lZ7tjgjoYhXU1hAgoGEmNcm6I2M6QGtgIDhQC1whyRJxwEu4M+yLB+0pkZ31gMqmDYf69D5/Dt3NO+ftW/lRbEekFgP6HBeD6ihoYH6+n3TEg639YCKi/cA+yo8FxSMpa6uTqsifaBrys4e1OvXFF0PqKvX1NzcrG3vjWvyeDwx16TX6wgGvWRmDhHrAXUGSZImA6uAX8uy/KokSdOB94ERsiw3dXBYHt2cB7S9pIH7/7OG68+ZwLhh3Z881hv01zkt0H9lE3KpeDwtNDfX43SmYTKZO7SE+uucFui/sh0OcimKQiDgp6GhmoSEJOLibO0eI9YDasseIAi8BiDL8veSJNUA+aiKqUexWU3Yg26UD9/Ge/pcrN0onyEQ9DeiHU5jYw2hUMfZnT5fMxUVew6VWF2iv8p2uMhlMBgPqHy6y4BWQLIs10iStBQ4CfhUkqR8IB0o6o32bFYjlrAf67qV+KdPEApIMGCIi7MdtPOZO/eUfmkxQv+V7UiXa0AroAhXAC9IkvQQEAAulGW5oTcaireaCOoiWXABMQ9IIBAIDsSAV0CyLO8E5hyKtkxGPf5QCBBp2AKBQHAwRCWEHsYXVFc+FBaQQCAQHBihgHoYb8BPWKdDOUCwViAQCARCAfU4voCXxccuJPmUeX0tikAgEPRrhALqYUJ+N26fsH4EAoHgYAgF1MME/S3k7/6R2g/e62tRBAKBoF8jFFAPE/K7SW8qo2Xd2r4WRSAQCPo1QgH1MCG/mxadmaDL1deiCAQCQb9GKKAeJuh34zFYCbUqFCgQCASCtggF1MOE/G48BguK14MSmZQqEAgEgrYIBdTDBP0tuAxx4HASjpQzFwgEAkFbBnwpHkmSdqOuiOqNbLpFluVPequ9kN/NusR8jj3rLAx2e281IxAIBIc9A14BRThLluWNB9/t5xPyuwFwe0UtOIFAIDgQwgXXwwT9bhwBF/GL/oVb7n9l1gUCgaC/cKRYQK9IkqQDvgH+1FvLMYBqAel0EL9nG4GqSpBGHfwggUAgOBJRFGVA/+Xn5w+O/GvJz89/Kj8//z8HOSZPURRl2rRpSnZ2tpKdna189dUK5auvVmifs7OzlbvuulepqmpSJkyYoG074YQTlezsbOXMa19RvjntDOWKSZOV7OxsZePG7cpbby2OOf7JJ59TqqqaYradd94FSlVVk3LeeRfEbK+qalKefPK5mG1vvbVY2bhxe8y2hQuvVaqqmjQ5srOzlQkTJrRpp6vXVFXVpCxceG3Mvv3hmu66694j8pqibfW3a2r9GxDP6ci7pttvv0uJkNfZ/lmnKEpf68BDhiRJ44D3ZFkeeoDd8oBdtbUuwuGu35vCwlH8cuGL/Pr750g5+WTSzj63m9L2LIWFo/rlyovQf2UTcnWN/ioX9F/ZBpJcer2OlBQ7wFBgd6eO6bJkhxGSJNkkSUqM/F8HnAf0eo0ch82Cz2Ql1CKqIQgEAkFHDPQYUAbwjiRJBsAAbAZ+39uNJsSbqI5LIdsm0rAFAoGgIwa0Aoosxz3xULebEG/m3ZwTOfHs2Ye6acFhwNI1JeRlORia5ehrUQSCPmVAu+D6Cke8CY8vSCAY7mtRBP0MRVF4/csilq0r62tRBII+56AKSJIkiyRJv5YkaeShEGggkGAzM6VhMyX/90BfiyLoZ/gCIQLBMC1esWihQHBQBSTLsg/4J5Dd++IMDBzxZuJCPgI7tomCpIIYXG61QkaLR1TKEAg664LbAOT3piADiYR4E41GOygKwYb6vhZH0I9ojiieFlGqSSDotAK6HrhZkqRfSJI0oBMXegJHvJlGkw2AQE1NH0sj6E80Rywgt3DBCQSdzoJbDMQD/wUUSZLqgZhZmrIsp/ewbIctCfFmmoxqCnawtraPpTk8UBSFT37Yy2QpjTRnXF+L02u4PH5AWEACAXReAf2D/RSOoGPiLAbcFjuutMHorNa+FuewoLHFz5tLi/AHQ5w280CFKg5vohaQxxciFA5j0ItEVMGRS6cUkCzLd/ayHAMKnU5HvD2OtWMvYNKk0X0tzmFBbZO6XFNTi7+PJeldXK2SD9zeIAnx5j6URtDTLF6+k8p6D5efVtDXohwWdCmeI0mSGRgHJAN1wAZZlgd2j9FNEuJNNLk7vjVPvruBETlOTp46+BBKdWhweQIY9DriLJ1/veqbfMDAV0DNrd6JFqGAusTDb65lipTO7An9MyG3rKaFD1bswRYnwuSdpdP2vyRJNwOVwA/AJ8CPQKUkSTf1kmyHNY54M0M3fcWeu25r852iKKwtqmXrnoGZIff4O+t5+RO5S8fUHSEWUNQFByIVuyv4/CE27qyjqKSxr0XpkDeXFhFWFNzeIEdSkeefQ6cUkCRJ1wH3A68CxwGjgTmRz/dLknRNbwl4uJIQb8YXVPCVlaGEYysiNLsDBEPhA1pIvY3LE+i1QHh1g4fd5U1dOqauWbWAGt0Du1Nu9gQwm9SfnZiM2nmqGzwAeHz9855V1rtZv6OWpAQLobCCX1RB6RSdtYCuAv4qy/JVsiwvk1WWybJ8FfAAIBTQfiTazFSErBAK8e6SnyiubNa+62q8o6re3eMjqufe38yz720+YJuh8IF/RA++uoYvVpfEbFMUBZcnSFWDh0Cw85NwjxQLyOUOkJEUD4hMuK5QWa8qIHc/VUB1jer7mz/YCYg0+87SWQU0GFjawXdfATk9Is0A4rhJgxhekAfA+tXb+ctLq/D61ZdS62w7YQEVVzbzx2e+Y7Vc3aPyVdW72dNKKbbmu80V/OGZ7/h2Q0WHx/v8IbYWN7B2e6xc/kCYYCiMokBlnafT8kQtILWGXverR0TvbX/F5QmQkRxRQH3kgttR1khpTUuftN1dohZQVxVQIBjixY+2YIpL7A2xNBojv+XMyLN1d2JwsWlXHWZbcq/K1d/prAIqBk7u4LuTIt8LWpHmjOOkEycA8P/GOQmFFSrq3ADURgLu/kAYrz/IV2tLWbOtfQXz0/YaFGDDzp6dT9Tk9tPU4m8zUisqbeSFD7cAsLW44xhVdaPaIeypdMVYZ62zvMpqO9/J1TV5Meh1qmwt3euYiyubufHJFRSV9s84QTis0OIJkJGkznPqq1Hyix9t5Z2vdvRJ292lKuqC6+I921nWxLJ15SRk9m42avSdzUqJKKCDKMpgKMxj76wnq+DUXpWrv9PZdI3HgMckSUoG3kZNRkgHzgYuRrjg2sWYnELCtKOwZqfDtgbKa93kZTqobdw3Sm9q8fPBit2kJcYxKT+tzTnW71ArKRxIGXSVQDCEx6daGRV1boZl71sW4KPv9mCzmhiUZmP73o478poG9RpcngB1TT5SEq3a5yjlte6DyrJ+Rw16k5VGl58Das9qAAAgAElEQVTBGXaKK100uf3a+bpCcaUr0m4LIwb17oi3O7i8ARTAabcQZzHg6sQo2e0NEm9t/2fa4PJR2+RleHbXrrXR5e9ShmJ/oKpefZe6agFFXXdGc3yPy9SaphY/Br2O1MTODS7KaloIBMPEOQf1qlz9nU5ZQLIsPwFcDswFlgCrgI8in6+QZfnJXpOwh5Ak6Q5JkhRJksYeqjb1ZjNZl11B1pRC9Dqd1iG3dhPVN/uob/JpI7zWNLp87CpvJsVhobrBS01j511aB6KxVZyloi7WSimtaWFkTiITRqRS2+Tt0KVV3Ure1q681p1q+UEsoJoGD39/az2DJ52NAuRlOtrI1xWqGtT72+Dqn3GkaAacPc5EvMVEi+fAnVR5bQtXP7qMHR1YdO99s4tH31rfJRlC4TAuT6DfBvM7oqp+XxJCV+KhlRGvg8Fs6xW5ojS1+HHYzNgig4WDKaDdFepvxurIJBw+cjPmOp2GLcvyc6ixoCHAjMi/g2VZ/mcvydZjSJI0CTiKPnIVGoJ+0pxWKiIdcm2TF0e8CYBd5c0oQEOzj0AwxPodNby7bCc/bKnU1ow5Y/ZwALbuaegReVq7uKJuQQB/IER1vYfsVBv5OWowdXsHaa/VjR7MRj16nU77McG+uEZqopWymlgLqLHFH2P9VUf+nzxkGgB5WQkR+bqnQKIxp4ZIPKm/4YrECezxJmxxxoPGCcpqWlAUkPe2/9wr6ty4PIGDJovEynB41aILKwrBUJjaJi9mo77LGWbR99vY2wrIrSqguKgCOoiC3xP5zeiN5nYHn0cKB7XDJUmyAuuBa2RZ/hjYG/k7LJAkyYJaSugCOk6k6DWqXnuF5u+/I2vWpZTX7bOA8rIcrN9Ry84ytYNXgJpGL298WRTjukpKsDC9IIPXvtjO1uJ6Zo3PAlQf8r+WbOGEyYNjXGidIdrB64h1k5XXulGA7FQbOek2LGYD20samD4mo805ahq8pCfFoUBMhl/UBTcyx8mPW6sIhxX0kdjOv5Zsodkd4Lb/maLdBwCd3gBAXubPU0DRUXKDa58CevPLInZXNHHzBZNi9g0EQzS1BLrl6usuUQsoIc6EzWo6aBp21JLbU9F+skh1xA3a4gnisHVuQmvUuuyOBbRyUwVjhiSRaLd0+djuEFYU/vD0SoZlO1AUGJxhZ0dpE25vEIvJ0KlzaC44S/cUUF2TF48/RGZy3AHLJjW6/CTazcRbohbQgQcXuyuasMeZcHkClFS5tOSF1oTCYd75eidOm5mTp+V2S/7OEv3d2uNMvdrO/nRmPSAv4AQO18T2u4H/yLK8qy8aNyYlEXI1MyhBT2WdG58/RJM7oHW2O8r2zZcprW6hos7NvKNyuXPBVC79xRiu+n/j0Ot0jMlLYl1RjTZy3by7jpWbKlm1tarLMkWz73LS7TEWUFkkM2pQqg2DXs+IbAdbixvadXlUN3hIc8YxJCMhpoPcp4ASCYbCWrKCoigUlTTGuO6iCigcVOXJSIonzmLolgJSFIXKSJygPmIBhRWFlZsqkPc2tMmse3/FHm57/vuflXHXVaL3JiFeddUcLA07qkjby1YMhsLUNe+Lw3WWqBL0+kNdcv3UNHp47v3NfN3NlVy/3VDOtxvKu3RMo8tPTaOXH7ao73hehjrQ8viCbVYcLqlycdOTK6hp9X6Fw4o2KDF0Mwb04Gs/cds/v+fGf6w4YIZlk9uPI96M0aDHbNIf0AIKhsLsrWph2uh0FCVMSbWrzT7+QIhH317Px98Xs7zVfWtw+bj5qRXs6uI8u4Px7HubeOa9TT16zs7Q2UjkK8AC4NNelKXHkSRpBjAV+ENXj50//wRKStQ5Lq+++jYAF1xwlvb95ZdfxZVXXs1JJx1DdbWawTZ69BgA7r77NhYteguAqc4kbhohEdi7mWAolZNOO5sRcxayZ/t6bNZkrbMEePbVD1Di81i65DXuve4VbfvatVsxNcu4PImcffltlK57l7MWqmG3N979kPtueAqAM844m9tv/wvnn38GW7aoc3zS0tL47LPlABz/q4sIeJuJT85l0PjTSbWFWF3UROHE0aAozP+fuzDo07n417+guqqClGFHM2Tab1j6UylfffAiny39Gk+9avxO/81TOEwtvPf6mwyefA7TZx3DTdctxO+cSMjv4dabLkU68Ubuuu9BnvzbnVx9w024M+YDMHHSWH5as5FV6zYT8AZprthCQobED98tw2Yx8+aixTx40wsHvaannnqcf7/9IblTzqf4x1cZdfIt6IBtO/dSWHghcc4cRs/9EwCnn3M+ZTvXa89p7Pw/4fWHmDP3NFpq1IywTz/9mi1bNnHttb8HIM45iOsf+YR7rzyeo48apz2P2bPn8NhjT3PNNVewbNlXMc/p7bff4J577tC2Pfrok4weXcDJJx9L1thTySyYx2OP3EvetAsoq6imsHBUm2t65pl/AJA77TekDjuaqnoPk6YWEg7s6wBrm7xExwVnn3cOuWkWXnttUcy71941JQ2ZytAZC9R3c/okQgFPzDVdfe3VVJkL2fPDf/A2lmvX9MRLixk281L++dIrJAaP1q4pyhlnnA3Q4XNaVpyE3mjhqgtva/f3dMFvb+T6Kxcwf94c7fdUMOUELCPORKeEUHQGnn/qPvKmX0RpRRUPv7aLrT99RcmaN9V7cOXfqG2yce6C31O7ayUAs46bTzDjF4Dqgove67Vrt/LmW2/w1op6KrZ8QnPFlpjnFOX0My6gyjyLUMMOGhnOBVf8gVD1aj77bDn3PvYSn36yhMbSdQBMOf9J/J5GCgtHMfa0e3nl9W+okxPb7SMe+PtLBENhnn/ibrLG/oKXXl/L0dKFMc8pLf84Bk86m6yUeIrLajXZZ8xdgM85lQcef4HvljyNLXU47ro9/LRm4wHfvdbPqb3fU8Ev78Pt8WjtAGzevLHNc+qo33vttUU89tjD3HXX7XQFXWcCepIkXQ/cCJSjJiFUElsdW5Fl+akutXwIkCTpD6gZetEhdQ6q7AtkWe5ImeYBu2prXd0KDhYWjmLt2q3aZ19pKXvuuBXdGRdy/3odJ08dzKc/7uWWCyby8icy5bVuUhOtNLvV+mluX5C/XjGD9HaWJHj+w818v7mSP/x6Mn977Sd8gRDpSXH89fIZbfYNhsJUN3jISlFdD1OmT2HSWf9HXlYCeRkJfLuxgnOPH8GLH23lgStmkOaM47G311PV4OGe300HVKvikbfWabEnRVH4+zWzCIYUrn/8Gy44cSST8tO46akVzD9qCGceO5zn3t/E9pJG/nThZG544lt+fVI+J0zO4btNFTz7vvrCP3jFDFKdcTz85lqa3QHe+8dlfPjJt2Qmx3P/f1Zj0OvauMzaw+MLcue/fqC6wcvYYcls3FnHkMwEiiubefamOSz5rph3l+0E4JL5o5kspdHiDeCIN3PVI8sIhRXOmjOc+UcNaff88y68iayCedx8/kRGDUk6qDwH458fbGbLnnoeumomb31VxKc/7OXZm+ag0+na3f/hN9ayaVcdCnDLBRORclUZCgtH8Z93v+XhN9TO7+ozxjGxnQxKUK3B1XIVJ0zOQafT8ekPxbz+ZRGA9txbs2l3HQ+9vpZzjx/BKa3cPm9+WcTHPxQzJi+JG8+b2G5b+7/7UcJhhSse+opgSOHBK2domWJRymtbuPW577nwFInjJu7LCvt2QznPf7iFa88aT2Wdm+GDErn336u57uwJPLFoA4NSbdyxYCoAd734I3sqmjlu4iAuPEWipMpFg8vHw2+uI8Vhpay8lFf+crZ27romLzc+uYLCEalcc9Z4ALaXNPDhyj2cOCWHgrxkNu2q4+E313Hz+RN579td1DX7uP+yo/D4gvzvkytId8Zx1yXTcHkCXPPocs47YSQnTx3Mbf/8nszkeK46YxztsWxdGS9+tJX7LzuKK257Cmnc0cyfMYQhGQkMyUwgrCj86dnvcMSbmTIqnde/2M6j18wiId7MU4s38uPWKo4em8nps4Zyy9MrtXa7i9sbYOHf1UHqE9fNJt5q7PBZHgi9XkdKih1gKLC7U8d08twPAVnAJODPwOPAE/v99TtkWf6rLMvZsiznybKcB5QApxxA+fQ4pvQ00Omwe9RO/Jv1qjmd7LDiiBSiTHPGkea04vYFsZoNpHYQlzhj9nCsZiP3vrwKXyBE/mAn1Q2eGFdElJWbKrj1ue9ZLavui5ThMwmGwpRUuWhs8eOIN5Gboa5Z9OJHW6lv9lFW00J26j5fuU6n47fzR5ORHMeoIepcpg07ajU3R6ozjmSHlfHDUli+vpxgKIzLE8QeZyLRZsZs1Gsut13l+9xI9RHXUn2Tj+QEC6GAR/OBO2zmTmfBvfHldmoavaQmWtm4sw4AabATRVETLTbsqCU3w47ZqKek2sXrX2znjhd+ZGtxPaHI4OJAtcXsqcOAfXOeDsT73+7i0bdUhRAMhduNsdQ0eEiLPFu71UQorOALdOwCbHD5tfjeT9treOnjrZobKBr/gX2rrO5POKzw9H838urn27VEkSZ3bDXu/amOuKz2T6HfGXH5tE4i6QivP8gPWyo1121Nk5dgSP1/e6n9m3erUwxKqmJdUdUNHnRAwdBkTp6Wq6WOVzd4CIbClNe2EFYUmtx+iiPXt6u8iS176rn9hR947YvtAAwf5GiThBB9BzfvrtOewbJ1ZazfUcvDb6zj4++LtXuWm5HAzHFZVNV7KCptZOlPpfj8IaoaPCiKormMHTY1fhJnNeL2BflidQkPvrqmzfXuLGvEZjWSlhSHt7GMqgYPL360lTeXqgODjTtrqar3cOKUHK0vqGn0ElYUtkTqR5bXtmjy/dyakuWt3PBRN/ahorNp2PqD/HUuIngEojeZSTntVyQWjCEnzY493sRZc4aTmmjVAsdpTqs2Eh2cbkffwYg4KcHCXZdMY3ReEjlpdo6dkI2i7Jsj0ZpoRtg/P9zCxp21pI2YjUGvw+sPsau8CYfNTF6mg4vnjWJHWSO3Pvcd1Q0eBqXG/lAT7Rb+8tvpXHf2BBzxJtYW1WhKJSrzsYWDaGrxs66oFpcngD3OhE6nI80Zt08BVTRpAdpocL22yUuKI1bZOmzmTsWAtu1tYNm6ck6Zlsu86epIXa/TafN/Sqtd7ChrpHBEKtmpNvZUNLNmWzUeX5DXvlB/6IUjUikqbWw3xhUKh4lPyQP2dfYH8haslqtZv6OWFm+ARct2cscLP7TZv7rRq90zWyTYe6D4TYPLx+B0O0kJFj79cS9fry3j0x/3RmTyaO9JNPNQURQee3u9Fhf85MdiLYtx0y5VQbeuvtGekoxmZLWODYbCYXZXRBRQk49w5Lr8gVC79+/fn2zj6f9u0tquaKXM2svoi3aqZftVZ6hu8JDssGA0qN1UdD5UtIqDPximpsHD5oiVOCrXyd4qFys3qRU8ymvdWMwGslNtGExWgqF9A7XGyDvoD4bZvFu9N3JxA+OHpyANdrL0p1L2VDSTnhRHvNXIZCkNi8nAfz7dxqc/7sWg12nx3Oj7mmhTkzPiLWp8b9OuOrYWN7R5xjtKmxg+KBG9Tkd98Wom56cxdlgyRaWNBIIhvlhditNuZlJ+WowCKq5sxuUJ4LCZKa91a89E3tuAzx/ib6/9pD3nkmpXpxNNWj+fyrp+poAkSbJKkvScJElHHQqBepOIJbTxULeb8svTiR81mrsumcr9lx3F/KOGoNPpNAsoNTEuRgEdiKQECzeeN5G7LpmqWSvtTfisa/ZijzNhMRl4+M11mOOTmBvpqGsavZrymz0hm7sWTGP0kCQU6HACp16nY8KIVDbsrOXbDeWYjHptND9ueDKJNjM/bKmkJaKAQFVQVQ0eQuEwxRXNTBiRAqhuIbc3iNcfInk/BZSaaKXFGzxgmaJgKMy/P5VJcVg4feZQJkvp6HU6UhOtpDrV832zoRxFgXHDU8hJsyPvbaAlkj1VWecmMzmewpGpuDwB9la5YpZJANhb5cJgtETul4c9Fc0s/PvyNqN0UDvikuoWFGBHaSM/baumptEb04kHgiEamn2kRp5z9P43tQSoqnfz1OKNMfFA1ZoM4LRbGJWbRKLNzLBsB99vrgSdTk0CSYrDaNBpHZzLE2BtUQ3vLt9Jo8vH4uW7mDgyldx0u9YxNbf4NcXVXqA8agFVtJrDVV7jxh8IMyzbQTAUpjnS4b6xtIj7/r2a+/+zRlMKq+VqTQFEO/bofRia5WB7SawCCocVbQS/f+WM6gZvjIswagG1VlSl1S1s3FWHzWpkzsRBhMIKKzdWMH54CkMyExiSbtfex9ZZh9EED6NBx0/ba6ht9FLT6KVgaDJHj8ukptHLuh21DMlQk4WsZiMXzZXw+oM0uwOcPE11eVXVuzWLPfpMbVYjbm9QsyxKWyUZuL0BSmtaGB75nXmbKrjqjHEcPzGHQDDM6m3VbNxVy6zx2RgN+lYKyKM9wzmF2Xj9IdYXqdVRPL4g73y9gy176vl2Yzlub5C7X1zFf7/pXN5VRZ0bvU6Hjn1Zg4eKzmbBnQeIpT27Sdjvx1eijlxb+/ujJntqKwsoN/LCHwydTqe5rdqb8FnX5CM7JZ57fjeda84cz+7vXoqJdbRO281IjufqM8fz2LXHUDC049pUhSNT8fhCbNpdz/knjMQcSYU16PVIuU62l6ijPVsrBVTd4KG0ugV/MMzYoSkYDXoamn2aKynZEZvSOyxLdTntLOs4y2ft9hpKq1s49/iRWMwGHDYz08ekMyYvCWckRXjNtmrscSaGZjrIiSh1s0nPOcepc6pG5CQyMkftBP7y0ique/wb3v92lxb3i7rm0pPiqGnwsmVPPR5fkM9Xt52BUFzl0qyC7zZVaj/i7SWNFFc28/H3xdQ0elFQrV1Qi9WCmnK+eU89P26t4pE312rpu9EReqLdzMXzJP72+6OZNz2XxhY/CRmjqG7wkO6MwxZJ5YV9llp5rZsnF28kGApzzvEjKBiqjq69flWxR2Vob4Qclb3JHdBkiWZcTZHSAdUKqm/2sXxdGSNyEqmsd/P3N9diSx3Gix9tITfDzpDMBDZFFFBlnZt4i2pFlNe6aWyVJr+nshm3L8iQzASa3YGYgUB1g0dT2ABmox6DXhejgIqrXKzfUcu4YSnauxMKKxxVkMGffjOJ686ZoCmg1pZIg8uPTgeFI9NYu71GqzgiDXYyKT8Ng15HMBRmSOa+3+OMgkz+evkMHrl6lrYmUWWdp5UFpD7TeIv6TKKu6r2tBi3RrNcR+02dyB/sRKeDN74sQlFgRoE69SHeaiLeYqSm0cumXXUMTrdrscDSmhbt9xotCrxlTz1b9tQRDIVZt6P98l1NLX4WLduhWYQVdW7SnFaSHdb+ZwFF+BJ1GQZBN2j6Zhl77ryNYE1NzPaoEkhNjCMvMyHGhdQZLGYDKQ5LjA83Sl2Tl2SHFXucicKRqdTt/p44i1FLbkhsZyG0g80BGJOXjM1qZOa4TI4tjF0UbGSOkwaXH7cvqJ0nPSkOfyCsjtpRf2ROu5kGl09LId7fAsrLdKDX6Tqc/Q+qe0EHmkUFcOkvC7ho7igc8Wb0Oh3BkMLYYcno9ToGp6mW4rhhKcwan8XYYcnMKMgkMzmeSflpTB2dzhQpnXeX7+KDlbsBtSae312vxdmiqdDfba5sM8cj2kGnOKzatRr0OopKGnn76x28ubRIc0dFBxpR67exxadNnC2vdXPrP7/n3WU7tfvjtFswGQ0YDXrGD08l3mIkfeScSBq8VZtLAmiVMvQ6HdtLGjlqTAYZSfEUDE0mFFaQixtoatlXDHX/GJCiKGqnHxl1R9+rXeVNxFmMjMlTO77aJi8ffb+HcBh+94sxXHvWBBpcfqQTb8Rg0PP7/zeOccNS2FXWjNsbpKLOTWZKPGOHJqMD7nl51b4SUxHr54RJOdo9APAFQjS2+GMsIJ1OXeTQ5QmgQ/UGfPVTKS5PgGljMkhJVO+HQa9j/LBUTEYDVrMRmzViAXkCfLe5Arc3SIPLR6LNzMlTBtPiCfDGl0XYrEZy0u3YrCbGRjr21gooKkOizUyKw4pep6OqwU2TWy3DE3URxlmNeP0hLc5YUt2CXFzPix9tYfPuOnQ6GLqfAoq3GsnLdNDo8pOXmaAlD4E6QC2rbqGotJGCvGSyU/allBeOSCXdqc7Hy82w0+jy8/kqVRlV1rljpj1E+WZDOR+s2KO5Q6MegczkuBir/VDQWQX0D2CBJEn/J0nS8ZIkFUiSNKb1X28KebgTN0q9Pe4tscsfjB+eypzCbIZkJDB8UCKPX3dMTBJAZ8hMsSEXN3DPy6u0itlhRaG+2UeSo+2EwaiLr7MTF1tjMRl44IoZLJg/uk3mVmvFuc8Fp3Zky9eXk5UST0qilaQEC/XNPq0g6/4xIIvZwOB0+wEtoIo6NymJVkzGtqFHvV5Hol29tvHDVAU1JDOBFIeF2ROyMRkN3HBOIaOHJKHT6Vh4xjgu+2UBV5xewMicRNZur0FRFLaXNNJSs5O0RCuNLX6KSho0hfrtxtgq4bvLm0m0mZkspaGgWi3jhqWwfmet5jZZHpk/E80Ac7SygBpc6iz6/z23kMHpdt5fsZsv15QCaBYdgMmo57hJg0gcNA6PL0SaMw671aTFgKKdzbETs9Hp4NQZeYA6J8tk1LNxVx3Nbj/p0WKo+1lATe4AvkCI8cPV+xaNDewsb2JoVoIme2m1i2Vry5hRkEG6M45h2Q4uPEXC767n+rMnkO6MoyAvibCisLW4noo6NxlJ8eRmJHDzBROxmo08894mWrwBVmyqIDfDzqghauWNqHUTtR72zwaNdvIOm5nB6XYaW/zYrEZVuel0TMpP46iCjJj6edEVSotKG3n2vc0sX18WmThqYUROIidOGYw/GGZkjlNzTx43aRApDgtDM9uf5B11j1XVe1SrNN6kHRvfqs5enMVISbWL/36zi2Xryvnkh70MTrdjNbedARNV8DMKMmO2pybGsW1vA8GQwpihSThs+ya85mbYGZOXhNVs4H/mqinU8t4GBkUGXRsj719Ng4fFy3cSCofZErFMd5Q0ElYUKus9ZKbEk5EcT2W955AuptdZBfQxagrzDcDnqJURNkT+Nkb+FXSAOSsLQ6IT95bYiV5JCRYumjsKk1F9DN0pEJmdYqO+2cfOsia+26x2jM0tfkJhheSEtl7TnJ+hgEB1CbSXJBGtnAD7fvDR0avLE6AgTx1ROu0W1QKKVL9ObEeO4YMc7Cxv6jANPjqi7gin3YIOGBtRQPFWE3/7/UzGDUvp8BidTseo3CT2VDZTVNpIfbOPpootmguotsnH0QWZDM1K0JRJlN0VTQzNcmhrwYwZkszInESaWvwoiqq4d5Q1YTLqNeVoMuqJtxhpagnQ4PLhtJsZNSSJ685Skz2iiQROe+z9OWP2MLZ++gCnzxrKUWMyVAsoYsnUNHpJiDdx3vEjuOuSadpgxmQ0MG5YCt9tqsAfDJNkt2AxGzQXXF2TlxeWbEGOFLwtyEvGoNdRUedW41tVLQzNchBvNRJnMfDV2jL8wTDHFu5LmZ49IZuN792qWQzDByViMRn4Zn059c0+7XlJuUlc+ssxeHwh/rFoA6XVLZwyLZdkhxWLyaDFgaLuxP3TxKO/EWeCRUuYmTo6Q0tUuHjeKH57aux42B6xgKIxqdLqFvWeR969M44dxtihycwcl6UdM354Kn/7/cwOC8GCauFX1nkormyOGUjZWh1TOCKV4koXcnGDlnXakZdj+ugMRuYkclRBbOWR1EQrCqrSy89xotPpyEqNR4c6oDxrzgjuWDCVvMwEkhLUAcvxk3Ii2aG1KIrCix9v5b1vd7Nartas8e2ljdQ1egkEw2Qmx5ORFI/HF4xZtbe36WyPJ9xvPwOdTkf8mDG4N2xACYfRHaCkR1eZf1QuI3MS+XFrFdtL1KoFUeti//gK7Ct3s7/l8XMx6PUMz3aweXe9ZgGlJsahQ50wNnaYqoCSEiys26HGcJISLFqZntYMz07kyzWllFS72sTEFEWhss6j1aprjxGDEkm0mbtcVkTKdfL+Clj0tTp3qKlsU0wHmJuZQJzVyGufb6e0poVBqTZcngAVtW6mj8kgf7ATR7yJaaPTNbdPfk4iDruFVVurSE20xihvNeXcF1FA6rPS69VR/Fdry9Dp1KoJrdHpdLjr9nD6rKEA+8WAPKQmxmEyGshJi01mmTk2U1vyIyFeHUG7vUFC4TBPv7eJopJG1hWpbrHMlHjSk+Ior3VTXKnGt4ZG4ispDisl1S2kOCwMG9RxCSijQc+JU3L4cOUeALJalZrJzUhg3LAUNuysJTXRyrTRahJJZko85TUtBIIhTVlErego0ZF/kt2ivRszCtqWimpNNCYZ7XhLa1w0unxairvFZOCGcwsPeI72yEiKZ9PuEhQFLjw5f5+MEQVkjzMh5Tq1pIzLTyugvNat3cv9yUm388ffTG6zPeoSzR+cqMVdC/KSMUdcjK3bHJXrZOWmSsYNTWZvZTMrNlWwaNlONu+uR6eD17/Yjj8YJsVhZWdZI0WRUmBZKTa8fjUdvT23XW/RKQUky/LXvS3IQMc2uoDmlSvwlezFmtv+xMfukGi3MGVUOo0tfn7cWkVdU6sAfzsW0PjhKfzpwsltfNs9wYhBiTEKyGTUk+Sw0NTiRxqsuhecdgv+QJh1O2piJjq2ZnikYysqbWyjgOqbffgCoQNaQOefOLJb8g8flIhBr0Pe20Buup013kYt0w9gSEYCeZkJvP75dn7cUknS1FwefXsdOp2OccNSsMeZ+Ps1xwAQCIYZPsjB3KOG0NDsY9XWqjaj+cRIynmDy69VAgeYLKXz1doyEm3mdhV0a+xxqgtOURRqGrxaQdf9GTc8RYsXRV04Hl+QD1bsoaikkaFZCewqb0aHOnDITI6nuLJZq1W4vwKaNoeaKqYAACAASURBVDqjw+kCUc48djjpzji+/KmUETmxo/5fHD2EDTtrOXXGEK3GWnaKjZWbKrji/75WMzJzEtsMIjQFlGBhyqg0UhMnaxllHWE1G1DCIQIRj2NpTQuBQLhd67srpCfFoSiqVTZj7D63WVTGzJR4bSAwJEON67SO7XSWqOsz6kUA+NUxw9rdd970IeRmJJDqjOOkqYPZUqxOrs1OtVE4IpUl3+1Bp4O503N55bNtvLV0B2lOK8MHOQiH4bSZeW0GL71Jl3w+kiTNA6agVsW+R5blYkmSZgNFsix3r0jUEYJt/ARy/vdmzFlZB9+5G0Q77R1ljdo8m/YsIF0XEx26wlEFmRRXumJ+ZEOz1EKSUfecM2Hfj/74VrPeW5PmjGNQqo2PvtvD0WMzY/zl0Syd9oo3/lwsJgNDsx0UlTQybngKi1GtFLNRj9VixGk3o9PpkHKdrNhYwZptNZTXtnDF6QVtRrUmo55bL1SLrkYn9+0/wdhhM7OrvInmFn+Mq03KdWKzGjtV9NMep05odfuC1DZ5mTIqvd39jAY9R43J4PPVJThsJuIs6mTJ7zdXMnZoMr/75RhueWol9jgjJqOeKVI6P22v4eMfiklKsGiuneTINbRXoLY9jpmQzTETsttsH5nj5IErZsTck1NnDCEl0QKo9zgap2tNtNq0M8GiWt2deJd1Oh1BfwsmqwOzSY8/oGZ/OX9mUdVoLG32hKyYdzQ+Yv1mJsczKM2GzWpkdmHbe9BZRuQkMn54SqfueU66XXOzZ6XY+Mtvp7FyYwXDBiViMen56Ls9DM1yaB6J+mYfF56cj0Gvx6DvWLH1Fp1SQJIkZQDvAZNRSywMBZ5GXd5gAeAFruwdEQcGBrud+NG9l6uRk6bO+C8qbUSv02Ey6g95ZdvM5HitrEmUK08fq6Uog+o6AdU3nupsW24I1A7jorkS9/9nDYuX7+K8E/ZZNBW9qIBATcMtKmnU4kW6iGsoOcGqdYbTxmTw8scyNquRa88ez9ihHceWQA2knzpjCBNHxpbLcdjM1EQqCzgT9nWGRoOec48fqa0QeyCi8baSKhehsNLGZdWak6cNJhAKMyjVTrzVSHWDh8p61X3oiDdz0SmStmz89DEZvL9iNxV17piFEo8em0m8xXjQ+WqdYX+LMDvVpi090hFR6yI5oWvKI+RzY7I6mJSfxneb1EzFn6uA8gc7OWZ8VhtLPuoOy0yOV+fhLZypxai6gz3OxHVnT+jWsUaDPmYAcNac4WSl2Eh3xpEQb0IHMbGvQ01nLaDHATswClUBtZ619/n/b+++45uu8weOv75J2qaDbtoSKJTVDyACKqA4mW7EeaKe5964T089F+5znXfuc5z3uzsXDhzn9g4VEUEQBceHslehE7qbJvn+/vimIV00LU3T8X4+Hn1AvvkmeX+/Sb7vfDZwRzOPEY3UbNpI1U+rSD2m45fhddht5PRLZO3WMtKSnKT2iWlxjrHOZLMZ2NgdR/++CQzMSAj00GrJ8AHJTB7n4tPvNjP1gAGB3lD5JVXERNkDv8g72pT9+hPlsDUoJV558phARxGASaOy2FleyyH79mtyEW2OYRicckTTC2twFVDji2H9shutqf+RUT/VUUtJHayqnPqeUrExjkCX5/oql+BqJJvNYNahg3n23Z8YHFStN9SV1OYVWDtSXFAnhLbwuK2xOAeNygokoKSEvauCi41xcN6xTZf6Tk9ycsoRQzjYfz6b660ZKccEjQU8a0YuzmhHoF0pEkJNy0cDt2qt19BwElKw5lfr3evKhqg6bzVFb86jrrTjltcONqx/Ept2lLNmy84m42u6ioTYKO48f2JIaxjNPGQwNsPg06W7B39uL6kiMzU2bMk1NdHJCYcMbtD2khY0bRJY1YknHjYkpOSzJ8HPmdLOX+P1Cah+Wpa+Ia5vFNxVODuz+dLMhJEZnH1kbrNVaJFSXwXX1vPlcVdhYDXk1/942dsSUEsMw+C4STlhe/6OMnFkZqDLfaS0pVzY0qyJ6UDvXdKvDZyDrfrVmnVrw/L8M8YPoH96PDsr3M22/3Q3KX1imDgyk4U/5lNZY43M35Bf3q6G3K6oYQmofb/G6xPQqnUlOOxGyD886rsz72nyW5thMGX/AYFBs13B6MGpHDqmX6D9JVTVO7cwbEASzmgH/fvGY7B7JhIROaEmoK+AK5VSwWW1+pLQ+VgzJYhWxGQPBLudmvXrwvL8SQkx3Hz2ARx/8KAGYzS6s6MmZlNb5+Xfn6zmHx9pqmo8zBjf/qnnu5L6EpDNMJp0tw5VfQKqrfNyztEjQm5rqG+nGLCHyW+7on5p8Zx/7Mg2t6nkr3yfm86ylvgYMyQNNTB5jyucis4RahvQH4CFWINO38ZKPhcppUYDo4FuP1FpZ7BFRRGTPZCaDeFbnDUmyt5qQ253MjCzD8dNGsQH32zEBGYdOrjNS5B3VfUloKSE1rtbtyQhNopZhw5mn5zUJl2d96S+BJTdiV1uI62+2nb6+Gym95AfMd1dqOOAVimlDgDuBM7Fqo47GfgcuEBrnReuAHsa5+AhVCxfhmmaXaKTQHdwyhFD2T+3Lz9vKGlx7FB3VF/q2ZvxKIZhBAaltkV9G1BH9GYTor1CHgektV4LnB3GWHqF9JNPJeOMsyT5tNHgfoktjiDvrqIcNuKdjog0VvdNtmapCNeYMCFC0fbJx8RescdajafeqkrqduwIdEwQvdPh41wRqQYb4krkMf8yz0JEiiSgCNn+9xeo1r8y8JbbiM6K3EAwEVmnTR4WsdeW5CMiTbqBREjG6Wdg2O1s/etj+GprW3+AEEL0MJKAIiQqvS+Z511AXcEOqn7u9FXChRAi4iQBRVD8qNHYnE4qV/4Y6VCEEKLTtasNyD/+5wjAAL7QWsuCdO1gOBwkHDABwyFNcUKI3qfNJSCl1GXAl8Bk4FhgiVLq8g6Oq9fIOu8CMs6U3u1CiN6nxZ/eSqk4rXVVM3f9AZiktdb+/c4H7gaeCk+Ie0cpNR9r+QgfUAFcqbVeEdmomvK53diipVeSEKL32FMJaLVS6qxmthtYF/N6jWfH7mrO0VqP1VrvBzwMvBjpgBrb9sxTbLrrDkyPJ9KhCCFEp9lTAjoTuE4p9Y1SakLQ9geBxUqp15VS72OVfB4IZ5B7Q2u9K+hmEg2TZ5eQeOBBuLfns+srWflcCNF7tJiAtNZfYi2//SLwrlLq/5RS/bTWTwJTsSYn/QSrOu7xTom2nZRSzyulNgH3AudEOp7G4sftR6waQdE7b+Otaq7WUwgheiDTNFv9y83NTczNzX0oNze3KDc395bc3NyYUB7X1f5yc3PPzs3N/aCV/XJM0zQnTpxoulwu0+VymQsWLDIXLFgUuO1yucy5c+81CwrKzLFjxwa2TZs23XS5XOacOVc32HfVqjxz3rz5DbY99dRzZkFBWeD25OG55sITTjbz3v7AnD37zAb7FhSUmU899VyDbfPmzTdXrcprsG3OnKvNgoKyQBwul8scO3Zsg9dpzzEVFJS165hcLpc5e/aZZkFBWViOae7ce3vlMdW/Vlc7puDvgLxPve+Ybr99rumXE+o12TDN0JtwlFLDgEeAMcANWus3wpUYw0UpVQ0M0FoXt7BLDrC+uLgCn6/tzVvjxo1gxYpf2/w40zRZf+P1OIcOxXXpFW1+fLji6gxdNTaJq226alzQdWPrSXHZbAZpaQlgdfraEMpj9tgLDrgFmAFEA0uBu7TWs5RS04E/K6WuBK7SWv/Qpkg7iVIqAUjRWm/2354JlPj/uhTDMOh/zfVEZfSNdChCCNEp9tQJ4QVgJlaJ5zYgC/hUKWVorT8DxgHz/Nv+FvZI2ycemKeUWqmUWgFcC8zUWnfJnnsx/ftji5Ku2EKI3mFPQ/CPAU7TWn8KoJT6GigGhgJrtNZe4Aml1L+xFqrrcrTWO+hmq7WWfvIR3upq0medFOlQhBAirPaUgH4FzlZKLQNqgEuASmBL8E5a61Lg6rBF2MvUbN5E5cofSZs5C0PWrBdC9GB7usKdAwwHioBy4EKsElFNZwTWW8XvOwZfRQU169dFOhQhhAirFktA/ql2Jiml4oFof0lHhFn8PvuCYVC58gdih0ZusTIhhAi3Vut4tNaVknw6jz0+nthhw6n8UZZoEEL0bNLI0AUlHDABR1qazA0nhOjRZCGaLihl+gxSps+IdBhCCBFWUgLqwmo2bZTVUoUQPZYkoC7KNE0KX/k3+c8+Re3WrZEORwghOpwkoC7KMAyyLrwEIzqabY8/hqe8LNIhCSFEh5IE1IVFpaXhmnM1nl072frow3grKyMdkhBCdBhJQF1c7JChuK64Cnf+NgrnvRbpcIQQosNIL7huIH70vuTccz+O1LRIhyKEEB1GSkDdRFR6XwybDdPrpXrtGsq/WxLpkIQQYq9ICagbqfh+OTv+7yW8lRVgmsQMzCE6IyPSYQkhRLtICagbie7nwlddRdyo0Rh2O6Wffozp8eDZtSvSoQkhRJtJCagbic7KYsijf8UWG0vF0iU4hw4j/29PU7Hie5InTyXtpFOwx8ZGOkwhhAiJJKBuxh4XB0CfiQcCkHLUMfhqa9n5v8/x7NqJ67I5kQxPCCFCJlVw3Vzs0GEMuPb3pM06iYpl31G5SqbuEUJ0D5KAeoiUo44hKjOLskWLIh2KEEKERKrgeghbVBQDrr8RR3IyYM0lZxhGhKMSQoiWSQmoB4lKTcWw2ShfuoQNt9yIr0ZWTxdCdF2SgHogR3IKdYWFlH+3BE9ZGb7a2kiHJIQQTUgC6oGcw4YR3c9F0dtvsv4P11Ox7DsAqvSvlH7yUYSjE0IIiySgHsgwDJKmTMW7axcJB4wnYfwEEuwOtj3+GDu/WIDp80U6RCGEkE4IPVXylGkkjNufqNRUTNPk/IE5+Gpryb78SnZ98T8qV62k/5XXRDpMIUQvJiWgHsowDKJSUwHY/tyzHJqWTp+JBxLTvz8+t5vKH1ZQV1gY4SiFEL2ZJKBeIOnwIyiorSH9xFMASBi3PwAVK5ZHMiwhRC8nCagXiBsxkjkrVxDVty8A0ZmZRA/IpuitN9j+9xfa9Fym10tdcVE4whRC9DKSgHop12VzSDzkMKrX5uHZWUrVr7/graqibMlitr/4PLWbNzX7uG1P/pWtf30M0zQ7OWIhRE8jCaiXis7MJPO3v2PwPQ9QV1TElof/RNXPP1H89puULVrIxrm3s+vrhYH9a7duoeKHFcSP3Q/31i3UrF8XweiFED2BJCCBM2cwRoyTyp9WknLUMWRdfCmxw3MpfPXf1JWWAlDy/rtsf+FvJIwdixETw64vv4hw1EKI7k4SkMBwOIhTiurVmuTJU0mceBCZ516AIzkF766deMvLqfh+OYmTDsGRnEKfCRMpW/glG++ZG+nQhRDdmIwDEgDEqhFU/vgDNZs24hw4iOjMTAbNvQfDZqP0E2vl1aTDDgcg/cSTiUrvi82/NlEwX50bs9aNPSGhsw9BCNHNSAlIABA/el8Aajft7nxg2GyYHg+Fr79CVEYmMQOyAWuuubTjTyBl6nRMnw9fTTUAvro61t90A8XvvdPq65UtXkTRW2+E4UiEEN1Fjy4BKaXSgH8CQ4FaYA1widZaRmA2EtN/AEP+/FccfRIbbPdWVhCTM5i0mbOaPMb0etlw2y3E7zuG+DFjKf3kI6IzMilbvIj0U3+DLSqqxdfb8dKLmB4PqTNP6PBjEUJ0Dz29BGQCD2qtldZ6DLAWeCDCMXVZjZMPgCMpmUG33kHC2HFN7jPsdmKysylbtJCCf/6D2s2brCXCKyupXPE9AAUv/5P8F/6Gz+1u8Nh+l1wGQO3GjWE4EiFEd9CjS0Ba6xJgQdCmxcBlkYmmZ0qeNgN3/jaMqGjSZp1E/JixOFLTKH5vPs6hQ9n11ZeYdXW4t24FIGbgQDJ/dx7OIcMAqF67ptXX8NXWYkRFYdh6+u8lIXqXHp2AgimlbFjJ591Ix9KTxOUqcu66r8G2rPMuYMtjj1D1yy8Me/xpypd8S+G81/CWlxE/dhy7FvyX4g/exxYbS00zCcg0TQpf+Tdxo/bBmZPDut9fS+pxM0k/6ZTOOiwhRGcwTbNX/OXm5j6Zm5s7Pzc319bKvjmmaZoTJ040XS6X6XK5zAULFpkLFiwK3Ha5XObcufeaBQVl5tixYwPbpk2bbrpcLnPOnKsb7LtqVZ45b978Btueeuo5s6CgrMG22bPPNAsKyszZs89ssL2goMx86qnnGmybN2++uWpVXoNtc+ZcbRYUlAXicLlc5tixY5u8TluPqaCgrM3HtN/gwS0e070HH2Z+c9a55us33mKeM3a/Jsd0yLDh5sITTjYXnnCyeeeFl5oLTzjZ/GjmiabL5TKPHznKPHfSwWZBQZk5d+697T6mjV8vM5876xyzfxd7n0I5pvrX6oj3qSOPKfg70NZj6sjPXld5n3rbMd1++1zTLyfU67LRG6ZUUUo9DIwBZmqtW1seNAdYX1xcgc/X9nMzbtwIVqz4te1BhllXictX52bNZRcTP3ZcYDmIceNG8O1/PiP/6SdJPWEW7q1bKfnPe2T/4RZih+dS8PI/2bXwK4Y98Qxb//Io3vJyBt2+d2OQNtx2C+78bWTffCuxQ4c1u09XOWeNSVxt11Vj60lx2WwGaWkJAIOBDSE9ps2RdTNKqXuBA4ATQ0g+IsxKP/oQAHui1eHBXVjAzcNHsOXhP+Henk/RG69TvnQJsWoEscNzAXAOHoLpdlPx/XJMtxv3tq1NFtWr+H4Zlat+DCkGb3k57h3bSThgfIvJRwgRfj06ASml9gFuAVzAIqXUCqXU2xEOq1dLOmIKziFDSZl+JACeoiIGOGPBbifj7HPpM34idYUFJOy3f+AxMYMGE5WVRfG786nOW43p8TSYkbs6bzXbnnycrY89iun1BrZ7q6qajaH8+2Xg85F63EwAfDU1HXZ8daWlsuKsECHq0Z0QtNY/AUak4xC7ORITGXjLbYHbcSNHMWfl9yz/+yoMh/VxTJt1EgRVDce4XAy+5wE2zr0NI8aJWVuDe+tWovtmAOAcOozY4blU562m6tdf8FVVUfzefNzbtuG64qoGyQygYukSojIzickeSP7zz1K7eTM5c+/Z62NzFxaw4eYbST/tdFKPOmavn0+Inq5Hl4BE92BCIPkA2GJisDmdDfbxVldTu2ULSYcfAYA7P996rM+HYbPR/7rfY4uNpXzxN5R98zUYNuxJSez872dNXq/vb84g48yzMQyD6Mws3Nu24q2q3OvjsMfHA1C7Yf1eP5cQvUGPLgGJnmPHP14E0yR+1D6kHHk0juRk3AUFbHnoAbIuuIi4ESNJOGA8lT/8wJCHHsX0eCj99GOK33mb6rw8dv7vc1KOPApnzmBisrOJwZpWKHbYcDBNatatJX70mL2K0R4XT1RWVoNqQCFEyyQBiW4heep06goLiR2eGygdlbz/Lt7yMqKz+gGQfuIpZMw+E8PhwHA4SDzkMDylpfjq3JQvWUzNurXEjdqHpMlTcA4cBFgdHLDZqF6T1yQBpUdHB0pYoSj/bgl4vdQVFnTgkQvRc0kVnOgW4nIVg267E5vTSdWvv7D6wnMpW7SQ5GnTcSQnA+BITsbmjA08Jio1lczfnUv8qH3of+3vqSsqZNeXC/AUFwf2sTmdxAzIpnpNwwGxpsfD7bmj2P7cMyHF56urY/vzf6OusBB3QYGsGCtECKQEJLqdugKrhBG/3/6kn/KbkB4Tv89okqfPoOqnn4gfM7bBfalHHwuG1Veleu0a7PHxlC3+hiynk9hcReXPPxE/ap89Pn/t5s2YHg/pp5y211V5QvQWkoBEt9PnwIOwxcWSsP/4Ns0P1/f0MwEwjIYdI/tMPBCwZgXJf/ZpPCVWCWn5zlKOyMuj+L13GPLIXzAMwxq9bTTtWFmzfq0/tklEpaY2ud9X52bLQw+QcvRx1KzJw56QQHQ/V5MeekL0JpKARLdji4mhz/iJbX5cc4mjsf5XXkPNxg04UlI487encczI66z2ozVrKJz3KvFjxpI8dToF/3yJqtWamAHZ9L/meip/WIEjJRV7nwR2frEA56BBOHMGB5634rul1KxbBx4PlT/+gHu71Ytv0F33EeNyhXwMVfpXJqU0TXBCdEeSgITwMwzD6iGXbfWQ85gmcf6qt81/uhdbfDyssmGLiaF86RLiRo6i6qdVlH70ATXr1pI6cxaGYaPgX/8g9bjjGySgnf/7nKisLBImTCRhwkTqtuez4bZbqPp5VZsSUOGr/+baobl4KyratOpsXWEhtri4QFdxIboC6YQgxB5EpaUH/p90+GRq1q3F9HqJ329/+l/7e5zDhoPNRs59D5I8dRqGw0FUWnqgnQqg6pefqVm3jpSp0zEMwxp/1M9FVEYmVT//BIBn5062PfMkO/750h7jyTzvQgDKlywO+RhM02TDbTez9uor8FZUhPQY944d1KxfF/JrCNEeUgISohWDH3gIIyoad/42Sj/8D9FZ/ayOC0D2jTc3aYeKysjAnZ+P6fGA3U7R/LdwpKSSeNjhDfZLmzkLIyaG2s2b2PLoQ3grKkg/+TTAajPK/9sz9LvoUgC2P/8sqcfNxDkoh/VVlcQs+prkqdNDit9TVGTFAnh27cKekNBi93LTNCl+5y1KPvwAfD5y7rmf6MysFp+7pTYx0zTB620wwFiIxqQEJEQrotL74khKwumfuLRy5e5JT5u7iEe7XNRu3sTmB611ktKOP4GMM8/CFhXdYL/ESQcTP2oU2555Eux2Bs29h9RjrMRWV1hE5Yrv2frnh9ny6ENULF+Gt7wMgAVFhdRuWI/bX8qq3bwJX23L8+xWr8kDYOCtdxDTvz8FL/+LTffdbb1OcXGDueuq81ZT8v579Nn/APpdctkek0/Zt4tZc8UleMvLG2z31daS/8yT7PjX/7X4WCFAEpAQIbNFRZF0xGSMqKg97pd+4sn0u/gyUo46BsMwiN93DAn7HdDsvsX/eZ+6HTvod/FlxLj6B7bHuFxknnM+NevX4S0vp+8ZZwW6dy8sKcKRmgb+NVUK35xH8XvvYJom2198noofVzR4jeo1edZ4J//gW1tcHLUbN+CrqWbT3XdS8O/diaLql5/BMMg4+5xAR4+q1ZqSDz9oMrap9NOPMd1udn31RZPtFcu+IzrTmhViT8lR9G5SPhaiDTLPPrfVfWzO2EDX7taYdXVknPlb4nJVk/uSDj2MxEkHY9jtDbaXezwMefCR3c/h8VD54woS9tufskULqfzxB2LvuT/QSSF58hTiRowMlNacQ4aAaVL2zSK8FeU4Bw8NVKXVbc8nZlAO9rjdnRXKFy9i15dfUFe4w5pDz1+tZtbWYjgcJM84qkEsOxf8l7hR+5A8dRrrbriW5CnTSJs5C9Pno+L75SSMHbf7Odow00RXUr7sOwy7nYRx+4XtNUyPp8dXYXa/d16IHiRj9pl7bMtpnHyakzB2HO5t2yj58D9gt+OtqqTwjdcC98dkD6TPhN3d1p2DhwBQ8tEH1r8fvE/x228C0O+Sy8n+/Y0NY/ztOaQeezy7vvyCnf/7HLCWsHBvzyf12OOxBZUIy5d/h3fnTpKnz8AWE0N0Zhbl3y0FoGzhV+Q//QTF779jPUedmw1//AOFr7+Kr6a61ePsSsoWfsn2F5/b4z5Vv/5C9br2deSozstj49134q3c+0lyuzJJQEJ0c/FjrV/hMa7+uC6/krTjT8Cw2zF9Ptz52yhfvgxfnTuwv6NPIlF9++IpLsaRmkpUWhoVK5YH7g+ezgisdq70k08lKjOLKm2tkulzu0maPJW4kfuw6+uFbHvycQDKFn1NVEZmoLow4YAJuLduoTovjwR/Eiz99FPqSkrw1dTgHDqM0k8/DqxQ2x6b/3Sf1Y7WBqbX6y8BhtYrsLGovn0DVaAtKXzjdYqCfgiYpkn+8882aENsSfnSxdQVFrRa3dvdSQISopuLzsggup+LmvXrSRg7jrQTTiTz7HMxbDaK353P9heeA2/DRfLqS12xuYr4fcfi3raNtddeRf5zz7R4UY0dOoyaNWswTRNHYiKZZ51N7PDh+GprqPh+Gf2dTlyXzcF1xVWBarXESQfjSE9n2zNP4KuuZuDtczFra1h/43WY7jr6XXgJ2Tffire8nJ1fLGj1WE2Ph9otm3ff9vmozltNxXdLQ55/z1NexpZHH2L7C3+j9JOPQnpM8OsVznvVatuqrsbXqITiraigrrAQX20ttZs3ETtsOMXvzqdo/lvU7dhO+eJv2PqXR/f8GqZJxYoV1hi0Hj6noCQgIXqAtJNOIeWoowO3TdOkbNHXlC9dQvKUqU3WV0qeNgPXVdeQPHU6CeMnEN3Phempgxa6VQOkHH0MA66/AfCv/OpfdqLP/uPBMJiUkoYtJoaY/rs7U9jj4+l/5TV4KyqoWL4M58BBJM84ioTxE3D4pyyKHTKU2BEjm5QMfDU11G7eTP6zTwU6MpR88D4b77yN2s2bAAI9AwHc27aGdK6K579Nzdo12BMTqV63NqTH1Nv15QJKP/4I011nnYeiwgb3F7xi9TCsXpMHXi/OocNwF+xg538/w5GWRtLkqVbce6hac2/ZjKekmJq1a1h/0w09emLbnt3CJUQv0Wf/hr3sqn7+KdBGkXLk0U32N2w2EsaMC9zOufu+Vl8juJfe1kceJNrlwnX5lTiSk4nKyOQ3pkmV/pU4NaLh4/oPYNAdd2PYrMSWcfoZTZ47efJU6goLAp0SajdvYvNDD5A8bQblS5fgHDyElCOPDnSsKF+6hJjsgTiSkhn8wENsuP2P1BUUENN/QKvH4aupJm7fMSQddjiGI7QqrrJvF1Otf6Fs8TdWB4sZR1L2zdfUFRYGZrwwTZOqX37GW14WqHqLHToMw+GgfPE3VP7wA4kHH8quBf+lLFk8rQAAFIVJREFU8scVJE46pNnXqljxPRgGydOPpPjtN6nbsT2w5EhPIyUgIXqguBEjieqbYS3el5jYYc9bvnwZW//yKO7t+YFxUQDJU6YB4EhKbvZxMS7XHi+iCeP2I2XGUWAYlC1exOY/3Ydhd5AydTpxI0dR8tEHmB4PyVOnE7fPaMqXfBsoGUSl92XY4083O7Grr85N6WefMDA2LrCt30WX4rr8ShLGjGt1lvN6tZs2suvLLzAcUWSedyHRmVlkXXAxzqFDA/t4y8sDnUZqN28mup8Le0ICcSNHAZD/zJPY42KxJydbSaYFvpoanEOG0ueA8YCV/MKhdts28l/4G+tuvI664qKwvEZrpAQkRA9k2O3k3PtAYJmJjlLywfvUblhPbK4iZdqMwPbkadM54aZr+Sqr5YGrrfHs3MmGO2/FV1GBc9hw+l10CfY+fUiaPIX8p5+ket1anDmD6TPxIHb8/Xlq1q2lctVKTLebvqed3uT5TNNkx0svUv7tYtKjGw4CNgzDaj/Sv2KLj8c5cJDV9rJ8GUVvv0HigZNImzkLgMpVK0k6Ygqxw3OxJyYSlZICWO1bYC3h4auqIn7fMQx+8FGqflqJPSERW6xV7WnYbCQdPpldXy7AkZxC/yuvISojE4Cc2Djyn3+WzHPOCwxUTj/1N3hKS3D4J50tee8d4tQI4kaMbPe5bY53106qV2s8JSVUr1nTYNqpziIlICF6KMNmC2kG8LZInjKN6P4D6HfpFQ3GqBiGQbl/up/22vXlAsw6D33POIvsG24KXBDj1EgwDHb973PWXHEJhmGQeOhh2JyxVK5YTu3WLdRs3MCGO26l6tdfAs9X+slHlH+7mLQTT+b7XTvxVlWx/YXn2Pzwn6zZHwyDbc8+xc7PP2sQg6d0J8Xvzqdm4wZMn4/8Z56k9JOPSBi3H7FDdpd4ardspuqXn9nyyINs/cuj1BUVWgOPR4/BmZPTYBaJjLPOZuhjT2BzOnEOysEeG4vp83HfyNGUL/6G2k2bqM5bTc2GDQBEpaZhGAbpJ59K0uGT8VaUU523GtPjoejtN6krbNj21Jzy75aw/uYb2fX1wgazXdSLGzmKnLvvB8Nodw/EvSUJSAgRsqRDDiVn7j0dWq1XL3XmLIY98TQp02Y0GP9kT0ggcdLBeMrLwTSJyR5I1rkXEJ2VhTs/n5j+/YnO6oe3soKit+Zhmiam10vpxx8St89oUo+biQlseegByr75Gl91dSA5xw4ZSvXavMBA3H4XXsKQBx7CnpjIjpdexL19u1UlNmhQk3hLPnifHf/3Ejl33QvA+ptuoPSTj5s9NsNubzB7eXVeHnkXn4/DZiPjt78jdugwCue9xva/P9/wnBx7PJm/O5eit9+k9LNPqF67hpL/vMf6m2+grqRkj+czfsw4TJ+XHX9/nqI3Xw9sL1/2HUXz38L0erFFRxPVNyOwPEhnkwQkhOgS6mcKb07W+RfhzBmM4XAQ3c9qS6r8cQWmx0O0awC2mBjSTzyFmnXrqFi6hKqff8JbVkby5CmB5+wz8SCABokgfvS+1G3fTt4lF1C7dSv2Pn2w9+lDxulnUrt5E6X+wboxg3KaxBSV3pe6kmIcKamkHHMcQMgDah0pVltZQW0NSUdMwb19OzXr1pJ48CHNnoPojEzqCnbgSE4mbvS+AGz9658DPRFNr5fCea+y5ZGHWH/TDdRs2ogtOprB9z9E4qRD2Pn5Z4F2noplS9n1xYJA9WzKUcfQZ/yEkOLuaJKAhBDdQs36ddiTkwNVf2WLvwEI9HxLPPgQYrKzKXj5X9jiE8g462zi9929/HrSYYcT3X8Aqf5kAZA0ZRrpJ58KPh8lH/0nsD1h/AQG3non9oQEDIejQQ/Aeo70dPB6KX7nbdJOmEXarJNImjI1pGOJSu/LoDvv5g8/r8RTWsKGW2+yjuHAg5rfPzML944dRGVkMuCa6+l32RzcWzaz68sFgFUaK/34I3w11cQEldYMm420k04BoPid+da0TSt/JH7M2MBYreQjJrdrgceOIJ0QhBBdnunxUK1/tSZh9cu68GKqJh0SuOAaNhv9LrmCTfffTcXSb+nbqLu3PSGBnLn3NNhmGAapxx5P/Lj9iUrf3Qhv2Gw4c3IofP0VYrIHNjsnmyMxyYrN68UWFR3otBCqmAHZVHq9geexJybiSE5pdt/ozExMt5vKlT8QO1yRsP8BpMw4CufgIbi3b6f4vXfoc+BBgeU7gkWlWiU0w26nOm81vupqEsbt7oJvejy4CwqISk1pMgtGuEkCEkJ0eYbDwYDrbyQqIyOwzRYV3WQy0OisLAbecrs1VU4btLQqbb+LL21xup74MWPJuuDiBvPstYfhcDDorvuwx8e1uE99r7ltf32MlKOOpu9pswMJ1lNWRlR6XzLOPLvFx6fPOgmAgldfxnA4iBs1OnBfzcYNbL7/HlxXXkPC2HEtPUVYSBWcEKJbiBs5KqSuwtGZmR02w7YjOYWYAdnN3mfYbNZs5R0wY3WMy9XiGCqwBrT2u/QKa98BAxvcZ+/Th5y77g1puXXDZiNWjcAWExPYVj8+KxI94aQEJIQQXZzN6cT0Wt3cY7IbJkTDMCDEJNjnoEkkJ/RpsM0eH489MZGqX34mft+xDaZSCjcpAQkhRDdQ9NYbAHs1LY9z4CCi/HPwBYsZmEPVT6so++brdj93e0gJSAghugF7XBye0tKwLFLnuvRy3AU7wjK+a08kAQkhRDeQfcvt0MyMBh3B5nTiHNh0sG24SQISQohuwNYDF6eTNiAhhBARIQlICCFEREgCEkIIERGSgIQQQkSEJCAhhBARIQlICCFEREg37KbsADZb+1aSHDBgQLsfG05dNS7ourFJXG3TVeOCrhtbT4oraH/7nvYLZpim2aYX6QUOBb6KdBBCCNFNHQYsDGVHSUBNxQATgHzAG+FYhBCiu7AD/YClQG0oD5AEJIQQIiKkE4IQQoiIkAQkhBAiIiQBCSGEiAhJQEIIISJCEpAQQoiIkAQkhBAiIiQBCSGEiAiZiqcDKKVGAY8Dk4CdwPPAXK11pw1kVUqdBpwNHAAkARp4WGv9StA+C4Ajmnl4rNa6JkxxnQv8vZm7LtNaP+PfxwBuBi4D0rEGsl2ltV4RjpiCYltA8+cD4GCt9TdKqQ1A47WKd2itszowjmHADcBBwGjgK6315Eb7hHSOOvKz2FpcSql+wHXAkcBQoBT4L3Cz1npb0H6Tgf818xJ/0lrf1NFx+ffZQAjvWyefr8k0fx4APtFaH+Xf71xa+c60Ma5Wrw3+/S4CbgSygZ+AG7XWnzfapz/wBDADqAFe9e9X1da4QBLQXlNKpQCfAT8Ds7C+iI9glS5v7cRQrgPWA9cCRcCxwMtKqXSt9eNB+/0PuKXRY0MatbyXpgLVQbfXBf3/JuA2rC/vr1jH8plSarTWensYY7ocSGy07S5gP6wLfL2XsS5S9dwdHMc+WO/XYiC6hX1aPUdh+Cy2FtcBwElYF+1vgUzgTmCRP66KRvufRcP3fWs7Ygolrnp7fN8icL6WYyW6YAOB14APm9l/T9+Ztmj12qCUmg08g/X+LQTOA95XSk3QWq/y7+MAPsY6j6cDycCj/n9/257AJAHtvUuBWOBkrXUZ8KlSKhG4Uyn1oH9bZ5iptS4Kuv1fpZQL68MX/CUs0Vov7qSYgi1t5oKEUsqJdXG9X2v9hH/bN8AGYA5hTOJa658bxRINjAde01p7gu7KD/M5e09r/Y4/hjewSjjBcYV6jjr6s7jHuLAuVCOCz5VSajnWL+xTgH802v/H+ovZXmotrnqtvW+der78z9cgHqXUYYAPeL2Z52v2O9MOoVwb5gL/0Frf7Y/rC6wfYjexO7mcBowEhmmt1/v3qwNeVUrN1VrntTUwaQPae8cAHzf6sL6K9cFuqXqnwzX6gNX7HsjorBja6WCsUkjgC6i1rgTewzq3neloIAV4pbUdO5LW2tfKLqGeow79LLYWl9Z6Z6NEjdZ6NVBFGD93IZyvUHXq+WrBGcAXwVWWHa21a4NSagiQS8PPlw+YR9PP19L65OM3H6tEdHR7YpMEtPdGYFWJBGitN2F9CUdEJKLdDsaqXgh2pFKqyv/3sVJqTCfFslYp5VFKaaXUJUHbR2BN+tr419MvdP75m41VLdR4NvTzlVJupdQupdQbSqnGbQvhFuo5ivhn0f95iqPp5w6sX95epdQGpdStSqmQp+1vp9bet4ieL6XUcKxSRks/eFr6znSE4GtD/bH+2mifX4BUpVTfoP0any83sJZ2ni9JQHsvBavxsrFS/30RoZSahlWv/WTQ5i+Aq4GjgIux6p+/UkrlhDGUfKy2i7OBmVhtBc8opa71358CVDTT6FsKxPmrxcJOKRXnj+81rXXwDL3vAFcA07DaXyZhnbOkzojLL9RzFNHPolLKBvwFK1F+EnTXLuAB4Fysz958rCqfR8MYTijvW6S/u2cAdcCbjba39p3ZK81cG+qPtfG5KG10f4efL2kD6hjNTSlutLA97PwJ5WXgHa31S/XbtdZ3BO32lVLqM6xfNNf4/zqc1vpjrIbLeh8qpWKAW5VSf/Fva+n8tXRfOMwEEmj0a1RrfXXQza+UUouAFViNtI91UmwQ+jmK5GfxfqwL/RFa67r6jVrr77GqfOp9ppSqBa5TSt3dQhXRXmnD+xbJ8zUbq/dbSfDG1r4ze1MF2dK1wa/xMYf98yUloL1XitULpLEkmv+1EFZKqVSsHjWbaKVnir/31NfA/p0QWrA3gFQgB+v89WmmOiYZqAq+kIXZbGCN1vq7Pe3kb0TXdO45C/UcReyzqJS6HKukcY7W+tsQHvIG1g/gTqkCbuF9i+T5GovVoB9qe2Pwd6a9r9nStaG+pNP4XNTf3hm0X3PnK5l2ni9JQHvvVxrVfyqlsoF4mtaphpW/Gul9rC6gx/kbqkMRqUWhTKxzZAeGNbqvSX1zuPirZY6hbZ0POvOchXqOIvJZVEqdgtWb6kat9WttfHhnf/aCXy+S393ZWF2s32nj49p1vlq5NtQfa+N2nBFYvWYLg/ZrfL6igSG083xJAtp7HwJHKaX6BG07HevD9UVnBeHvoz8PGA4co7UuCOExmcAhwLIwh9fYKVjjETYCi4AyrC6e9XHVt8c0NzYiHE7CWgm31QSklBoNKDr3nIV6jjr9s+gfXPlv4Amt9cNteOgpgAf4MRxxNdbC+xbJ7+7pWN22Q+1mHfydaZPWrg1a63XAahp+vmz+240/XxMadeY4Aeu781Fb4wJpA+oIzwBXAW8ppf6E9WvgTuDRThwDBPAU1gCzq7F6rhwUdN/3WF+++7E+iBuxOiDcjDUGIWxtGUqpN4ElWBcaO9YX73SsUfw+oEYp9QBwm1KqlN2DLG00HL8UTrOBH7TWvzSK/Tisqor3gW1Yv/5uxarCeKmjXtyfTI713+wPJCqlTvXf/kBrXRXiOerQz2JrcWHNNDDfH89rjT5zhVrrtf7neRooxBrc6/Y/5xzgMa11cRjimkJo71unnq/62QL852kw1nvY3PO09p1pqz1eG7TWtVjH/S9lzSDxNXAOVsI6M2jfN4A/Yp2v27CqKv8MvNyeMUAgCWivaa1L/b1KnsAal7ET6025s5NDOdL/71+auW8wUIzVWHg/kAaUAwuAE/1dT8NFA+djTe9hYHX9/J3W+p9B+zyAdTG92R/bd8AMrfWOMMYFgFIqHaun1G3N3L0Za6zEY1j13MVYv/Ru6eAfFxlYPwyC1d8ejDXgtNVzFIbPYmtxHYh1ERqLddEK9g+sXm9gdee9EGskfjSwBrge+GuY4grpfYvA+drg//9srJ6BLZXwQ/nOtEVr14YNWutXlFIJwB+wvgs/AccHDxzWWtcppY7GOl+vY82g8ipW21+7GKYZqep/IYQQvZm0AQkhhIgISUBCCCEiQhKQEEKIiJAEJIQQIiIkAQkhhIgISUBCCCEiQsYBCdELqN3LQe/bQYvCCbHXpAQkhBAiIiQBCSGEiAipghMijJRShwL3AhOwJrl8C7hOa12ulDoX+DswEWtxtvFY08j8QWv9dqPnmYM1l9dA/z5Paq3/3GifMf7XOgzru/0z8Eet9adBu6UrpeqXWi4AHtZaP9WhBy1EiKQEJESYKKUOAT4HtgOnYi36dyxW0gn2Gta0/CcDK4F5/vVi6p/nIqxJR9/FmgF7HvCIUuqmoH1GYM3H1g+4FGuG77ex5hML9hzwg//+BcCTSqmJe3+0QrSdlICECJ8HgEVa69PrNyiltgKf+5cHqPd8/VIGSqmPsUouNwOz/dPi3wm8pLW+3r//J/41jG5WSj2mta4B7sCa4PIwrXW1f7/gkk+9V7TW9/hfawFWQjsZa/ZlITqVlICECAP/1PyTgNeVUo76P2AhUAccELR7oLrNP93+O1jVcgADABdNZ1l+DUgE9vXfngq8FpR8WvJJ0GvVAXn+1xCi00kCEiI8UrDWcnkKK+HU/9UCUTSsGmu8eGABVlUaQf82Xpqi/naq/980ID+EuBovnewGnCE8TogOJ1VwQoTHTqzlk+/EWiStsW3sXqclA2vNGoJu1yeT/KBtwTL9/5b4/y1md7ISoluQBCREGGitK5VSiwGltb6ruX2UUvX/PQlr0bb6pZBnsbtNZgtWsmq8PPJvsJbpXum//TnwG6XUH/1tQkJ0eZKAhAifG7E6HPiwljMux+pGfRzW0sb1LlRKuYFVwEXAMOAMsNqElFJ3As8qpYqxOhYcAVyGtcJnfbKZi7Xk9ZdKqUewSkT7AcVa6xfDepRCtJO0AQkRJlrrhcDhQF/gn1jLPt+INY4nuE1nNlYpaD7W8tana62/D3qe54Cr/Pu8j5WcrtdaPxC0jwYOBYqA57E6NpwKbAzT4Qmx12RJbiEiJGggah+tdUWEwxGi00kJSAghRERIAhJCCBERUgUnhBAiIqQEJIQQIiIkAQkhhIgISUBCCCEiQhKQEEKIiJAEJIQQIiIkAQkhhIiI/wfhRXaLKLMEbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "textsize = 15\n",
    "marker=5\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax1.plot(range(0, nb_epochs, nb_its_dev), cost_dev[::nb_its_dev], 'b-')\n",
    "ax1.plot(pred_cost_train, 'r--')\n",
    "ax1.set_ylabel('Cross Entropy')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "plt.title('classification costs')\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/cost.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(dpi=100)\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_ylabel('% error')\n",
    "ax2.semilogy(range(0, nb_epochs, nb_its_dev), 100 * err_dev[::nb_its_dev], 'b-')\n",
    "ax2.semilogy(100 * err_train, 'r--')\n",
    "plt.xlabel('epoch')\n",
    "plt.grid(b=True, which='major', color='k', linestyle='-')\n",
    "plt.grid(b=True, which='minor', color='k', linestyle='--')\n",
    "ax2.get_yaxis().set_minor_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "ax2.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
    "lgd = plt.legend(['test error', 'train error'], markerscale=marker, prop={'size': textsize, 'weight': 'normal'})\n",
    "ax = plt.gca()\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "    ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "plt.savefig(results_dir + '/err.png',  bbox_extra_artists=(lgd,), bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def save_object(obj, filename):\n",
    "#     with open(filename, 'wb') as output:  # Overwrites any existing file.\n",
    "#         pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)\n",
    "        \n",
    "# save_object(net.weight_set_samples, models_dir+'/state_dicts.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "from __future__ import division\n",
    "import time\n",
    "import torch.utils.data\n",
    "from torchvision import transforms, datasets\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "models_dir = 'models_pSGLD_MNIST'\n",
    "results_dir = 'results_pSGLD_MNIST'\n",
    "\n",
    "mkdir(models_dir)\n",
    "mkdir(results_dir)\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# train config\n",
    "NTrainPointsMNIST = 60000\n",
    "batch_size = 128\n",
    "nb_epochs = 200 # We can do less iterations as this method has faster convergence\n",
    "log_interval = 1\n",
    "\n",
    "# ------------------------------------------------------------------------------------------------------\n",
    "# dataset\n",
    "cprint('c', '\\nData:')\n",
    "\n",
    "\n",
    "# load data\n",
    "\n",
    "# data augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.1307,), std=(0.3081,))\n",
    "])\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "trainset = datasets.MNIST(root='../data', train=True, download=True, transform=transform_train)\n",
    "valset = datasets.MNIST(root='../data', train=False, download=True, transform=transform_test)\n",
    "\n",
    "if use_cuda:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=3)\n",
    "\n",
    "else:\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, pin_memory=False,\n",
    "                                              num_workers=3)\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=3)\n",
    "\n",
    "\n",
    "## ---------------------------------------------------------------------------------------------------------------------\n",
    "# net dims\n",
    "cprint('c', '\\nNetwork:')\n",
    "\n",
    "lr = 1e-4 # 1e-5 for sgld\n",
    "prior_sig = 0.1\n",
    "########################################################################################\n",
    "net = Net_langevin(lr=lr, channels_in=1, side_in=28, cuda=use_cuda, classes=10, N_train=NTrainPointsMNIST, prior_sig=prior_sig)\n",
    "\n",
    "with open(models_dir+'/state_dicts.pkl', 'rb') as input:\n",
    "    net.weight_set_samples = pickle.load(input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## inference with sampling on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m    Loglike = -661.259827, err = 0.017600\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "batch_size = 200\n",
    "\n",
    "if use_cuda:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4)\n",
    "\n",
    "else:\n",
    "    valloader = torch.utils.data.DataLoader(valset, batch_size=batch_size, shuffle=False, pin_memory=False,\n",
    "                                            num_workers=4)\n",
    "test_cost = 0  # Note that these are per sample\n",
    "test_err = 0\n",
    "nb_samples = 0\n",
    "test_predictions = np.zeros((10000, 10))\n",
    "\n",
    "Nsamples = 90\n",
    "\n",
    "net.set_mode_train(False)\n",
    "\n",
    "for j, (x, y) in enumerate(valloader):\n",
    "    cost, err, probs = net.sample_eval(x, y, Nsamples, logits=False) # , logits=True\n",
    "\n",
    "    test_cost += cost\n",
    "    test_err += err.cpu().numpy()\n",
    "    test_predictions[nb_samples:nb_samples+len(x), :] = probs.numpy()\n",
    "    nb_samples += len(x)\n",
    "\n",
    "# test_cost /= nb_samples\n",
    "test_err /= nb_samples\n",
    "cprint('b', '    Loglike = %5.6f, err = %1.6f\\n' % (-test_cost, test_err))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## rotations, Bayesian [IGNORE THIS FOR NOW]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First load data into numpy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 1, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "x_dev = []\n",
    "y_dev = []\n",
    "for x, y in valloader:\n",
    "    x_dev.append(x.cpu().numpy())\n",
    "    y_dev.append(y.cpu().numpy())\n",
    "\n",
    "x_dev = np.concatenate(x_dev)\n",
    "y_dev = np.concatenate(y_dev)\n",
    "print(x_dev.shape)\n",
    "print(y_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 1, 28, 28)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/homes/ja666/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:83: MatplotlibDeprecationWarning: The set_color_cycle function was deprecated in version 1.5. Use `.set_prop_cycle` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "#############\n",
    "im_ind = 90\n",
    "Nsamples = 90\n",
    "#############\n",
    "\n",
    "angle = 0\n",
    "plt.figure()\n",
    "plt.imshow( ndim.interpolation.rotate(x_dev[im_ind,0,:,:], 0, reshape=False))\n",
    "plt.title('original image')\n",
    "# plt.savefig('original_digit.png')\n",
    "\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 10\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "ims = []\n",
    "predictions = []\n",
    "# percentile_dist_confidence = []\n",
    "x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "\n",
    "fig = plt.figure(figsize=(steps, 8), dpi=80)\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "\n",
    "for i in range(len(rotations)):\n",
    "    \n",
    "    angle = rotations[i]\n",
    "    x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "    \n",
    "    \n",
    "    ax = fig.add_subplot(3, (steps-1), 2*(steps-1)+i)\n",
    "    ax.imshow(x_rot[0,:,:])\n",
    "    ax.axis('off')\n",
    "    ax.set_xticklabels([])\n",
    "    ax.set_yticklabels([])\n",
    "    ims.append(x_rot[:,:,:])\n",
    "    \n",
    "ims = np.concatenate(ims)\n",
    "net.set_mode_train(False)\n",
    "y = np.ones(ims.shape[0])*y\n",
    "ims = np.expand_dims(ims, axis=1)\n",
    "cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False) # , logits=True\n",
    "\n",
    "predictions = probs.numpy()\n",
    "    \n",
    "    \n",
    "    \n",
    "textsize = 20\n",
    "lw = 5\n",
    "    \n",
    "print(ims.shape)\n",
    "ims = ims[:,0,:,:]\n",
    "# predictions = np.concatenate(predictions)\n",
    "#print(percentile_dist_confidence)\n",
    "\n",
    "c = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',\n",
    "     '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']  \n",
    "                                         \n",
    "\n",
    "# c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff',\n",
    "#      '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
    "\n",
    "ax0 = plt.subplot2grid((3, steps-1), (0, 0), rowspan=2, colspan=steps-1)\n",
    "#ax0 = fig.add_subplot(2, 1, 1)\n",
    "plt.gca().set_color_cycle(c)\n",
    "ax0.plot(rotations, predictions, linewidth=lw)\n",
    "\n",
    "\n",
    "##########################\n",
    "# Dots at max\n",
    "\n",
    "for i in range(predictions.shape[1]):\n",
    "  \n",
    "    selections = (predictions[:,i] == predictions.max(axis=1))\n",
    "    for n in range(len(selections)):\n",
    "        if selections[n]:\n",
    "            ax0.plot(rotations[n], predictions[n, i], 'o', c=c[i], markersize=15.0)\n",
    "##########################  \n",
    "\n",
    "lgd = ax0.legend(['prob 0', 'prob 1', 'prob 2',\n",
    "            'prob 3', 'prob 4', 'prob 5',\n",
    "            'prob 6', 'prob 7', 'prob 8',\n",
    "            'prob 9'], loc='upper right', prop={'size': textsize, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "plt.xlabel('rotation angle')\n",
    "# plt.ylabel('probability')\n",
    "plt.title('True class: %d, Nsamples %d' % (y[0], Nsamples))\n",
    "# ax0.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)\n",
    "\n",
    "for item in ([ax0.title, ax0.xaxis.label, ax0.yaxis.label] +\n",
    "             ax0.get_xticklabels() + ax0.get_yticklabels()):\n",
    "    item.set_fontsize(textsize)\n",
    "    item.set_weight('normal')\n",
    "\n",
    "# plt.savefig('percentile_label_probabilities.png', bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "\n",
    "# files.download('percentile_label_probabilities.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
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     "output_type": "stream",
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    }
   ],
   "source": [
    "## ROTATIONS marginloss percentile distance\n",
    "import matplotlib\n",
    "from torch.autograd import Variable\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "    \n",
    "        \n",
    "###########################################\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.ndimage as ndim\n",
    "import matplotlib.colors as mcolors\n",
    "conv = mcolors.ColorConverter().to_rgb\n",
    "\n",
    "Nsamples = 90\n",
    "\n",
    "s_rot = 0\n",
    "end_rot = 179\n",
    "steps = 16\n",
    "rotations = (np.linspace(s_rot, end_rot, steps)).astype(int)            \n",
    "  \n",
    "\n",
    "all_preds = np.zeros((len(x_dev), steps, 10))\n",
    "all_sample_preds = np.zeros((len(x_dev), Nsamples, steps, 10))\n",
    "\n",
    "# DO ROTATIONS ON OUR IMAGE\n",
    "for im_ind in range(len(x_dev)):\n",
    "    x, y = x_dev[im_ind], y_dev[im_ind]\n",
    "    print(im_ind)\n",
    "    \n",
    "    ims = []\n",
    "    predictions = []\n",
    "    for i in range(len(rotations)):\n",
    "\n",
    "        angle = rotations[i]\n",
    "        x_rot = np.expand_dims(ndim.interpolation.rotate(x[0, :, :], angle, reshape=False, cval=-0.42421296), 0)\n",
    "        ims.append(x_rot[:,:,:])\n",
    "    \n",
    "    ims = np.concatenate(ims)\n",
    "    net.set_mode_train(False)\n",
    "    y = np.ones(ims.shape[0])*y\n",
    "    ims = np.expand_dims(ims, axis=1)\n",
    "    \n",
    "#     cost, err, probs = net.sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples, logits=False)\n",
    "    sample_probs = net.all_sample_eval(torch.from_numpy(ims), torch.from_numpy(y), Nsamples=Nsamples)\n",
    "    probs = sample_probs.mean(dim=0)\n",
    "    \n",
    "    all_sample_preds[im_ind, :, :, :] = sample_probs.cpu().numpy()\n",
    "    predictions = probs.cpu().numpy()\n",
    "    all_preds[im_ind, :, :] = predictions\n",
    "   \n",
    "    \n",
    "all_preds_entropy = -(all_preds * np.log(all_preds)).sum(axis=2)\n",
    "mean_angle_entropy = all_preds_entropy.mean(axis=0)\n",
    "std_angle_entropy = all_preds_entropy.std(axis=0)\n",
    "\n",
    "correct_preds = np.zeros((len(x_dev), steps))\n",
    "for i in range(len(x_dev)):\n",
    "    correct_preds[i,:] = all_preds[i,:,y_dev[i]]\n",
    "    \n",
    "correct_mean = correct_preds.mean(axis=0)\n",
    "correct_std = correct_preds.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(results_dir+'/correct_preds.npy', correct_preds)\n",
    "np.save(results_dir+'/all_preds.npy', all_preds)\n",
    "np.save(results_dir+'/all_sample_preds.npy', all_sample_preds) #all_sample_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\n",
    "    ax = ax if ax is not None else plt.gca()\n",
    "    if color is None:\n",
    "        color = ax._get_lines.color_cycle.next()\n",
    "    if np.isscalar(yerr) or len(yerr) == len(y):\n",
    "        ymin = y - yerr\n",
    "        ymax = y + yerr\n",
    "    elif len(yerr) == 2:\n",
    "        ymin, ymax = yerr\n",
    "    ax.plot(x, y, color=color)\n",
    "    ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "\n",
    "errorfill(rotations, correct_mean, yerr=correct_std, color=c[2])\n",
    "          \n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel('rotation angle')\n",
    "lgd = plt.legend(['correct class', 'posterior predictive entropy'], loc='upper right',\n",
    "                 prop={'size': 15, 'weight': 'normal'}, bbox_to_anchor=(1.4,1))\n",
    "ax = plt.gca()\n",
    "ax2 = ax.twinx()\n",
    "errorfill(rotations, mean_angle_entropy, yerr=std_angle_entropy, color=c[3], ax=ax2)\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + [ax2.title, ax2.xaxis.label, ax2.yaxis.label] +\n",
    "            ax.get_xticklabels() + ax.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "    item.set_fontsize(15)\n",
    "    item.set_weight('normal')\n",
    "plt.autoscale(enable=True, axis='x', tight=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight histogram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23928000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Total parameters: 2392800, samples: 10')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = 'pSGLD'\n",
    "Nsamples = 10\n",
    "# mkdir('weight_samples')\n",
    "# weight_vector = net.get_weight_samples(Nsamples=Nsamples)\n",
    "# np.save(results_dir+'/weight_samples_'+name+'.npy', weight_vector)\n",
    "\n",
    "print(weight_vector.shape)\n",
    "\n",
    "fig = plt.figure(dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "sns.distplot(weight_vector, 500, norm_hist=False, label=name, ax=ax)\n",
    "# ax.hist(weight_vector, bins=70, density=True);\n",
    "ax.set_xlim((-1, 1))\n",
    "\n",
    "ax.set_ylabel('Density')\n",
    "ax.legend()\n",
    "plt.title('Total parameters: %d, samples: %d' % (len(weight_vector)/Nsamples, Nsamples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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